{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xdsf78IH3_c7"
   },
   "source": [
    "# Fairness Simulation Study 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vaM1zvthPzxe"
   },
   "source": [
    "Click the ipynb links to run examples in [Google Colab](https://colab.research.google.com/github/SelfExplainML/PiML-Toolbox/blob/main/examples/Example_Fairness_SimuStudy2.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uGNfAjXUbwyE"
   },
   "source": [
    "## Data Description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "executionInfo": {
     "elapsed": 465,
     "status": "ok",
     "timestamp": 1668682899022,
     "user": {
      "displayName": "Aijun Zhang",
      "userId": "04897292400908543781"
     },
     "user_tz": -480
    },
    "id": "yN_CSxXZPyzi",
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "This example demonstrates the use of PiML for fairness testing. We first simulate a credit decisioning data with hypothesized features `Mortgage`, `Balance`, `Amount Past Due`, `Delinquency status`, `Credit Inquiry`, `Open Trade`, `Utilization`, as well as demographic features `Gender` and `Race`. The response `Status` is a binary indicator, and this is a classification problem. \n",
    "\n",
    "**Sample Size**: 20k, **Columns Num**: 10. \n",
    "\n",
    "**Features:**\n",
    "\n",
    "`Mortgage` *(Numerical)*: Applicant’s mortgage size.\n",
    "\n",
    "`Balance` *(Numerical)*: Average last 12 months credit card balance.\n",
    "\n",
    "`Amount Past Due` *(Numerical)*: The minimum required payment that was not applied to the account as of the last payment due date.\n",
    "\n",
    "`Delinquency Status` *(Ordinal)*: 0: current, 1: < 30 day delinquent, 2: 30-60 day delinquent, 3: 60-90 day and so on.\n",
    "\n",
    "`Credit Inquiry` *(Ordinal)*: Number of credit inquiries in the last 12 months.\n",
    "\n",
    "`Open Trade` *(Ordinal)*: Number of open credit accounts. \n",
    "\n",
    "`Utilization` *(Numerical)*: % credit utilization, the sum of all your balances, divided by the sum of your cards' credit limits.\n",
    "\n",
    "**Demographic Features:**\n",
    "\n",
    "Demographic features in Credit data cannot be used for modeling.\n",
    "\n",
    "`Gender` *(Categorical)*: Two kinds of gender.\n",
    "\n",
    "`Race` *(Categorical)*: Two kinds of race. \n",
    "\n",
    "**Target Response:**\n",
    "\n",
    "`Status` *(Categorical)*: 0: default (should not be approved) and 1: non-default (should be approved). The 0/1 ratio is nearly 1:5. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4kcbghi53_dB"
   },
   "source": [
    "## Load and Prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c51bbf332054893ab4c735d995eed74",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
       "            return false;\n",
       "        }"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from piml import Experiment\n",
    "exp = Experiment()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 428,
     "referenced_widgets": [
      "36d750a8938d4e38803a676ff828cfe1",
      "f0df14f15d9e4193b013dd21d8b5df84",
      "faba79a88d484a52bbc233936616a98e"
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    "executionInfo": {
     "elapsed": 644,
     "status": "ok",
     "timestamp": 1668682935480,
     "user": {
      "displayName": "Aijun Zhang",
      "userId": "04897292400908543781"
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     "user_tz": -480
    },
    "id": "1HXFMmmB3_dF",
    "outputId": "4c6f2b04-0fec-480e-e26b-b50a46fc4496"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6f984a43a8b34b1abe42bf950c77cd92",
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      "text/plain": [
       "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
       "            return false;\n",
       "        }"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
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       "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "\n",
       "        IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
       "            return false;\n",
       "        }"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
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     },
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     "output_type": "display_data"
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    {
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      },
      "text/plain": [
       "VBox(children=(Dropdown(layout=Layout(width='20%'), options=('Select Data', 'CoCircles', 'Friedman', 'BikeShar…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Choose SimuCredit\n",
    "exp.data_loader()"
   ]
  },
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    "executionInfo": {
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     "user": {
      "displayName": "Aijun Zhang",
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    "id": "RTcvcydS3_dH",
    "outputId": "6132ad1b-e35c-4107-e831-2926bfe48808"
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       "\n",
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   "source": [
    "# Exclude features one-by-one: \"Gender\", \"Race\" (demographic variables); \n",
    "# Excluded features will show in grey color in the table.\n",
    "exp.data_summary()"
   ]
  },
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    },
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     "timestamp": 1668682951959,
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      "displayName": "Aijun Zhang",
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    },
    "id": "iQBVjZlmDxWk",
    "outputId": "985a6338-af95-444e-b936-2bb3b5696d36"
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      "application/javascript": [
       "\n",
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       "<IPython.core.display.Javascript object>"
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       "\n",
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      "text/plain": [
       "VBox(children=(HBox(children=(VBox(children=(HTML(value='<p>Target Variable:</p>'), HTML(value='<p>Split Metho…"
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     },
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   ],
   "source": [
    "# Prepare dataset with Test Ratio = 0.2\n",
    "exp.data_prepare()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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    "executionInfo": {
     "elapsed": 1709,
     "status": "ok",
     "timestamp": 1668682966251,
     "user": {
      "displayName": "Aijun Zhang",
      "userId": "04897292400908543781"
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     "user_tz": -480
    },
    "id": "gskZJQ2GPyzq",
    "outputId": "2703d34d-5fc5-42d2-934e-e96f56954271"
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cc89e135580a4d299fd1c9a833001c24",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
       "            return false;\n",
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      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
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     },
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       "model_id": "2cf179e10599426d8db2b2d341819b26",
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      "text/plain": [
       "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
       "            return false;\n",
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      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
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      "application/vnd.jupyter.widget-view+json": {
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      },
      "text/plain": [
       "VBox(children=(HBox(children=(VBox(children=(HTML(value='<h4>Univariate:</h4>'), HBox(children=(Dropdown(layou…"
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   ],
   "source": [
    "# Exploratory Data Analysis\n",
    "exp.eda()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FGk06E-9bwyL"
   },
   "source": [
    "## Train ML Model(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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     "metadata": {},
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    },
    {
     "data": {
      "application/javascript": [
       "\n",
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      ],
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<link rel=\"stylesheet\" href=\"https://stackpath.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css\"> "
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     },
     "metadata": {},
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    },
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       "model_id": "7311f64b48fb42f2a2b3387eca162c70",
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      },
      "text/plain": [
       "VBox(children=(Box(children=(Box(children=(HTML(value=\"<h4 style='margin: 10px 0px;'>Choose Model</h4>\"), Box(…"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train and register GLM, XGB-depth2\n",
    "exp.model_train()"
   ]
  },
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    },
    "id": "WOe59Icm3_dK",
    "outputId": "d4f2bfec-8000-418f-9c24-8ac32d215c09"
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   "outputs": [
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   "source": [
    "# Manual train and register XGB-depth7\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "exp.model_train(XGBClassifier(max_depth=7), name='XGB7')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lu3mpYH33_dK"
   },
   "source": [
    "## Fairness Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. First select a registered model (in this case, XGB2)  \n",
    "2. Group Setting:  \n",
    "    - Set Add Category = \"Gender\", select \"1.0\" as reference, select \"0.0\" as protected, then click \"Add\".  \n",
    "    - Set Add Category = \"Race\",  select \"1.0\" as reference, select \"0.0\" as protected, then click \"Add\".  \n",
    "3. Distribution Shift:\n",
    "    - Set distance metric to 'PSI'\n",
    "    \n",
    "To evaluate the distribution shift between the samples of reference groups and protected groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
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       "            return false;\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp.model_fairness()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Metrics Tab:\n",
    "    - Select a metric (AIR, by default) and set the threshold. (e.g. 0.8)\n",
    "    - Set the favorable threshold (0.5, by defaut) and favorable class. (1 or 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "exp.model_fairness()"
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  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Segmented Metrics:\n",
    "    - Select the Balance as the segment feature and the metric AIR, and set the metric threshold.\n",
    "    - If the segment feature is numerical, set the number of bins. (5 by default)\n",
    "    \n",
    "   We can find that the higher balance, the lower AIR of Gender and Race."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
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   "source": [
    "exp.model_fairness()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Debiasing/unfairness mitigation by Feature Binning\n",
    "   - 6.1 Select a fairness metric (AIR by default) and a performance metric (F1)\n",
    "   - 6.2 Select an attribute (Balance), binning method (Quantile by default) and number of bins (5 by default) \n",
    "   - 6.3 Click the button \"ADD\" to apply the binning setting to the data. \n",
    "   - 6.2 & 6.3 can be repeat many time for different attribute.\n",
    "   - Click button \"CLEAR ALL\" could remove all the record."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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   "source": [
    "exp.model_fairness()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Debiasing/unfairness mitigation by Threshold Adjustment:\n",
    "    \n",
    "   - Select a fairness metric (AIR by default) and a performance metric. (ACC by default)\n",
    "   - Set the favorable threshold and class.  \n",
    "   - The number of threshold values is 20. (default for low-code) \n",
    "   - Check the fairness and performance metrics for varying thresholds.\n",
    "   \n",
    "   For this model, when we choose threshold as 0.37, the model can get both good fairness and performance."
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fairness Testing Comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. First select registered models (in this case, GLM, XGB2, XGB7)  \n",
    "2. Group Setting:  \n",
    "    - Set Add Category = \"Gender\", select \"1.0\" as reference, select \"0.0\" as protected, then click \"Add\".  \n",
    "    - Set Add Category = \"Race\",  select \"1.0\" as reference, select \"0.0\" as protected, then click \"Add\".    \n",
    "3. Metrics Tab:  \n",
    "    - Select a metric (AIR, by default) and set the threshold. (e.g. 0.8)  \n",
    "    - Set the favorable threshold (0.5, by defaut) and favorable class. (1 or 0)  "
   ]
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       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x324 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        },
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\n",
          "text/plain": "<Figure size 432x324 with 1 Axes>"
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          "text/plain": "<pandas.io.formats.style.Styler at 0x2c766952ee0>"
         },
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         "output_type": "display_data"
        }
       ]
      }
     },
     "22c3f91896f94556927983a1b828cb8d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "22c7c1a19c7a47588784c3cd4db0cacf": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "CheckboxModel",
      "state": {
       "description": "Original Scale",
       "disabled": true,
       "indent": false,
       "layout": "IPY_MODEL_67cd6c6f6c0f4acea756e44da37794d1",
       "style": "IPY_MODEL_fd103df8e67046dd8b118aef6db6cf10",
       "value": true
      }
     },
     "22c84490f4fd4eaf912ebc3ddd819e5c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": "initial"
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     "22c9cac5d8e949479648723196bb83fe": {
      "model_module": "@jupyter-widgets/output",
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      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_d67481d5abba41de9559a0a0aae2a59e",
       "outputs": [
        {
         "data": {
          "application/vnd.jupyter.widget-view+json": {
           "model_id": "",
           "version_major": 2,
           "version_minor": 0
          },
          "text/plain": "Box(children=(VBox(children=(HTML(value='<h4 style=\"margin-top: -5px; margin-bottom: -5px\">Response Setting:</…"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "22caad8031604e6b947a7dd2727e6dd2": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
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        "IPY_MODEL_cc60748261b1498c85a72600467ea999"
       ],
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      }
     },
     "22d2c8496d26495c9204210279553a49": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
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        "IPY_MODEL_7f36833cb70d40ad99be1007fe616926",
        "IPY_MODEL_8a5567bc1efe40be909b969ec93acf26",
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        "IPY_MODEL_a245866d22894d74bf3c21887c8cd710",
        "IPY_MODEL_9cda54f7e132457280887c4b6ea0ee6c",
        "IPY_MODEL_623abc69444c41d7bc2b976b9385eafb",
        "IPY_MODEL_c52b0fe85da54be09dbaea7096bab1e7",
        "IPY_MODEL_fab72997742f4a2794aa7247f65cde07"
       ],
       "layout": "IPY_MODEL_ea1214b04ce245569ac63153e34b7b5b"
      }
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     "22d3b1a447874363a17a87056202617e": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
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      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_f651234cdfd94921b85e3ffe83e55c73"
      }
     },
     "28a6062ffa024d8e942e12543101334b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_7e94ac45daba4526b06dfeedc5bbbe33",
       "style": "IPY_MODEL_fb8c95ece9034db1ab124e2d48a3ebf2",
       "value": "<p class='notification info'>No group selected</p>"
      }
     },
     "28af05de16d94b36849458e294e4dd82": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "28af20d9dfad40299504f81a0b9785fd": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "width": "40%"
      }
     },
     "28b201dd294045ed9b54fa149a274398": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_b398d76b449d4bd6b34b51babce5324f",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x432 with 1 Axes>"
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       "style": "IPY_MODEL_33995bda98dd4c9c942f297900217059",
       "value": "<h4 style=\"margin-top: -5px; margin-bottom: -5px\">Group Setting:</h4>"
      }
     },
     "3c5650dd8d274157a2c222438107f99f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "3c56f9a5194b4558a57d1fb83dd793ea": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_e8dbf39c9f7048599c8e6052cb1b3e2c",
       "style": "IPY_MODEL_9bc2ebe337b344c7b104e65cdf09e579",
       "value": "<div class='divider'></div>"
      }
     },
     "3c627d7a5ef64ac6a1b829019eea0d11": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DropdownModel",
      "state": {
       "_options_labels": [
        "AIR",
        "Precision Ratio",
        "Recall Ratio"
       ],
       "index": 0,
       "layout": "IPY_MODEL_9ed0e675004e4d45a045806b2edd2eaf",
       "style": "IPY_MODEL_71f5067306d84a7b8a138aa6377e7df0"
      }
     },
     "3c6604d957714d46bd02fd785eeb2ca7": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_693dad87baf54c139a612ec609856490",
       "outputs": [
        {
         "data": {
          "image/png": 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        {
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          "text/plain": "<Figure size 864x324 with 2 Axes>"
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     "43eecd84f2bf4845a420bfb0b76bd6a8": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
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        "IPY_MODEL_9e72d435812841d7b81a92b2b0ef7c85"
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          "text/plain": "<Figure size 1200x450 with 2 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
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          "text/plain": "<Figure size 1200x450 with 2 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
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       "value": "Binning Feature:"
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\n",
          "text/plain": "<Figure size 432x324 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        },
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          "text/plain": "<Figure size 1200x450 with 4 Axes>"
         },
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          "text/html": "<style type=\"text/css\">\n</style>\n<table id=\"T_766bb\">\n  <thead>\n    <tr>\n      <th id=\"T_766bb_level0_col0\" class=\"col_heading level0 col0\" >Group Index</th>\n      <th id=\"T_766bb_level0_col1\" class=\"col_heading level0 col1\" >Group Category</th>\n      <th id=\"T_766bb_level0_col2\" class=\"col_heading level0 col2\" >Reference Group</th>\n      <th id=\"T_766bb_level0_col3\" class=\"col_heading level0 col3\" >Protected Group</th>\n      <th id=\"T_766bb_level0_col4\" class=\"col_heading level0 col4\" >Threshold</th>\n      <th id=\"T_766bb_level0_col5\" class=\"col_heading level0 col5\" >AIR</th>\n      <th id=\"T_766bb_level0_col6\" class=\"col_heading level0 col6\" >ACC</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_766bb_row0_col0\" class=\"data row0 col0\" >0</td>\n      <td id=\"T_766bb_row0_col1\" class=\"data row0 col1\" >Gender</td>\n      <td id=\"T_766bb_row0_col2\" class=\"data row0 col2\" >1.0</td>\n      <td id=\"T_766bb_row0_col3\" class=\"data row0 col3\" >0.0</td>\n      <td id=\"T_766bb_row0_col4\" class=\"data row0 col4\" >0.50</td>\n      <td id=\"T_766bb_row0_col5\" class=\"data row0 col5\" >0.842758</td>\n      <td id=\"T_766bb_row0_col6\" class=\"data row0 col6\" >0.674380</td>\n    </tr>\n    <tr>\n      <td id=\"T_766bb_row1_col0\" class=\"data row1 col0\" >1</td>\n      <td id=\"T_766bb_row1_col1\" class=\"data row1 col1\" >Race</td>\n      <td id=\"T_766bb_row1_col2\" class=\"data row1 col2\" >1.0</td>\n      <td id=\"T_766bb_row1_col3\" class=\"data row1 col3\" >0.0</td>\n      <td id=\"T_766bb_row1_col4\" class=\"data row1 col4\" >0.50</td>\n      <td id=\"T_766bb_row1_col5\" class=\"data row1 col5\" >0.610387</td>\n      <td id=\"T_766bb_row1_col6\" class=\"data row1 col6\" >0.674380</td>\n    </tr>\n  </tbody>\n</table>\n",
          "text/plain": "<pandas.io.formats.style.Styler at 0x172ba84e2b0>"
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      "state": {
       "width": "30%"
      }
     },
     "60bf3bb6049a4db6afe0c1ec13376335": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_a4ddadc7f26d408faacedffaae286324"
      }
     },
     "60c42c2d13514ce5a7fa1d2bc6a16337": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_e35f6dae61fb4b58984307f1e655058c",
       "outputs": [
        {
         "data": {
          "image/png": 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vertical-align: middle;\n            appearance: none;\n            text-decoration: none;\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 550;\n            font-size: 12px;\n            line-height: 1.75;\n            letter-spacing: 0.02857em;\n            text-transform: uppercase;\n            min-width: 64px;\n            padding: 6px 16px;\n            border-radius: 4px;\n            transition: background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n            color: rgb(255, 255, 255);\n            background-color: #D71E28;\n            box-shadow: rgb(0 0 0 / 20%) 0px 3px 1px -2px, rgb(0 0 0 / 14%) 0px 2px 2px 0px, rgb(0 0 0 / 12%) 0px 1px 5px 0px;\n        }\n\n        .jp-OutputArea-output {\n            flex-grow: 1;\n            flex-shrink: 1;\n            margin: 0px;\n        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\n",
          "text/plain": "<Figure size 600x600 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "6d11cad9dac2401b9e29631b9555c820": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_96dcbabce3454cd3b4a15b7f7ffd5328",
       "style": "IPY_MODEL_94b63c1b09f24a75883fd4b0c3d61f5c",
       "value": "Max_iterations:"
      }
     },
     "6d17048a4b804d3baa6ba0d3d0bdc58e": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "6d17da284a314371a3fab1957e251e1d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "6d1956a7f4e24313bee23b3a037b3d97": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_dc9037995f5348b8be441baf00957775",
       "style": "IPY_MODEL_b13b6241ee2140c993e398cc25ba177f",
       "value": "Fairness Metric:"
      }
     },
     "6d247c9c83794046be63c4099ed4e896": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_5b07eea0f4484182b1c712c471026ea4",
       "style": "IPY_MODEL_de3f5e8fc7574735adb31d3013ff5ba5",
       "value": "\n        <style>\n\n        .left-label {\n            width: 30%;\n        }\n\n        .card-panel {\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            padding: 24px;\n            margin: 0.5rem 0 1rem 0;\n            border-radius: 2px;\n            background-color: #fff;\n        }\n\n        .card {\n            position: relative;\n            margin: 0.5rem 0.1rem 1rem 0.2rem;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 24px;\n            color: white;\n        }\n\n        .card .card-title {\n            font-size: 24px;\n            font-weight: 300;\n        }\n\n        @media only screen and (min-width: 800px) {\n            /* For notebook: */\n            .card {width: 99% !important;}\n        }\n\n        @media only screen and (min-width: 1400px) {\n            /* For colab: */\n            .card {width: 99% !important;}\n        }\n\n        .blue-grey.darken-1 {\n            background-color: #546e7a !important;\n        }\n\n        .small-card {\n            position: relative;\n            margin: 0.5rem 0 1rem 0;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 0px 5px 0px 5px;\n            color: white;\n        }\n\n        .inner-card {\n            position: relative;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 20px;\n            color: white;\n            margin: 0px 1px 1px 1px;\n        }\n\n        .card-with-tab {\n            position: relative;\n            margin: 0.5rem 0.1rem 1rem 0.2rem;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 24px;\n            color: white;\n        }\n\n        .card-with-tab button {\n            width: auto;\n            padding: 12px 40px;\n        }\n\n        .list {\n            padding: 0px 10px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            margin: 0px 1px 60px 1px;\n        }\n\n        .list-item {\n            padding: 10px 0px;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .divider {\n            margin: 10px 0px;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .hr {\n            margin: 0;\n            -webkit-flex-shrink: 0;\n            -ms-flex-negative: 0;\n            flex-shrink: 0;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .notification {\n            transition: box-shadow 300ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n            border-radius: 4px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 500;\n            font-size: 12px;\n            line-height: 1.43;\n            letter-spacing: 0.01071em;\n            background-color: rgb(2, 136, 209);\n            display: flex;\n            padding: 6px 16px;\n            color: rgb(255, 255, 255);\n            margin-top: 10px;\n        }\n\n        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  padding: 24px;\n            color: white;\n        }\n\n        .card .card-title {\n            font-size: 24px;\n            font-weight: 300;\n        }\n\n        @media only screen and (min-width: 800px) {\n            /* For notebook: */\n            .card {width: 99% !important;}\n        }\n\n        @media only screen and (min-width: 1400px) {\n            /* For colab: */\n            .card {width: 99% !important;}\n        }\n\n        .blue-grey.darken-1 {\n            background-color: #546e7a !important;\n        }\n\n        .small-card {\n            position: relative;\n            margin: 0.5rem 0 1rem 0;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 0px 5px 0px 5px;\n            color: white;\n        }\n\n        .inner-card {\n            position: relative;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 20px;\n            color: white;\n            margin: 0px 1px 1px 1px;\n        }\n\n        .card-with-tab {\n            position: relative;\n            margin: 0.5rem 0.1rem 1rem 0.2rem;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 24px;\n            color: white;\n        }\n\n        .card-with-tab button {\n            width: auto;\n            padding: 12px 40px;\n        }\n\n        .list {\n            padding: 0px 10px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            margin: 0px 1px 60px 1px;\n        }\n\n        .list-item {\n            padding: 10px 0px;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .divider {\n            margin: 10px 0px;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .hr {\n            margin: 0;\n            -webkit-flex-shrink: 0;\n            -ms-flex-negative: 0;\n            flex-shrink: 0;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .notification {\n            transition: box-shadow 300ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n            border-radius: 4px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 500;\n            font-size: 12px;\n            line-height: 1.43;\n            letter-spacing: 0.01071em;\n            background-color: rgb(2, 136, 209);\n            display: flex;\n            padding: 6px 16px;\n            color: rgb(255, 255, 255);\n            margin-top: 10px;\n        }\n\n        .success {\n            background-color: #EDF7ED;\n            color: #1E4620;\n        }\n\n        .error {\n            background-color: #FDEDED;\n            color: #5F2120;\n        }\n\n        .info {\n            background-color: #E5F6FD;\n            color: #528298;\n        }\n\n        .warn {\n            background-color: #FFF4E5;\n            color: #663C00;\n        }\n\n        .margin-top-20 {\n            margin-top: 20px;\n        }\n\n        .hide {\n            display: none;\n        }\n\n        .pbtn {\n            display: inline-flex;\n            -webkit-box-align: center;\n            align-items: center;\n            -webkit-box-pack: center;\n            justify-content: center;\n            position: relative;\n            box-sizing: border-box;\n            -webkit-tap-highlight-color: transparent;\n            outline: 0px;\n            border: 0px;\n            margin: 0px;\n            cursor: pointer;\n            user-select: none;\n            vertical-align: middle;\n            appearance: none;\n            text-decoration: none;\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 550;\n            font-size: 12px;\n            line-height: 1.75;\n            letter-spacing: 0.02857em;\n            text-transform: uppercase;\n            min-width: 64px;\n            padding: 6px 16px;\n            border-radius: 4px;\n            transition: background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n            color: rgb(255, 255, 255);\n            background-color: #D71E28;\n            box-shadow: rgb(0 0 0 / 20%) 0px 3px 1px -2px, rgb(0 0 0 / 14%) 0px 2px 2px 0px, rgb(0 0 0 / 12%) 0px 1px 5px 0px;\n        }\n\n        .jp-OutputArea-output {\n            flex-grow: 1;\n            flex-shrink: 1;\n            margin: 0px;\n        }\n\n        .output_subarea {\n            padding: 0px !important;\n        }\n\n        .ptable, .pth, .ptd {\n            border: none;\n        }\n\n        .pth, .ptd {\n            max-width: 150px;\n            white-space: nowrap;\n            overflow: hidden;\n            -o-text-overflow: ellipsis;\n            -ms-text-overflow: ellipsis;\n            text-overflow: ellipsis;\n        }\n\n        .ptable {\n            display: table;\n            border-collapse: collapse;\n            border-spacing: 0;\n        }\n\n        .ptable.striped .ptr {\n            border-bottom: none;\n        }\n\n        .ptable.striped > .ptbody > .ptr:nth-child(odd) {\n            background-color: rgba(242, 242, 242, 0.5);\n        }\n\n        .ptable.striped > .ptbody > .ptr > .ptd {\n            border-radius: 0;\n        }\n\n        .ptable.highlight > .ptbody > .ptr {\n            -webkit-transition: background-color .25s ease;\n            transition: background-color .25s ease;\n        }\n\n        .ptable.highlight > .ptbody > .ptr:hover {\n            background-color: rgba(242, 242, 242, 0.5);\n        }\n\n        .ptable.centered .pthead .ptr .pth, .ptable.centered .ptbody .ptr .ptd {\n            text-align: center;\n        }\n\n        .ptr {\n            border-bottom: 1px solid rgba(0, 0, 0, 0.12);\n        }\n\n        .ptr.hightlight {\n            background-color: yellow !important;\n        }\n\n        .ptd, .pth {\n            padding: 0.2rem 0.6rem;\n            display: table-cell;\n            text-align: right;\n            vertical-align: middle;\n            border-radius: 2px;\n            position: relative;\n        }\n\n        .ptr.grey {\n            background-color: grey !important;\n        }\n\n        .ptr.strikeout .ptd:before {\n            content: \" \";\n            position: absolute;\n            top: 50%;\n            left: 0;\n            border-bottom: 1px solid #111;\n            width: 100%;\n        }\n\n        .widget-toggle-button {\n            display: inline-flex;\n            -webkit-box-align: center;\n            align-items: center;\n            -webkit-box-pack: center;\n            justify-content: center;\n            box-sizing: border-box;\n            -webkit-tap-highlight-color: transparent;\n            background-color: transparent !important;\n            outline: 0px;\n            border: 0px;\n            border-bottom: 2px solid #E4E4E4 !important;\n            box-shadow: none !important;\n            margin: 0px !important;\n            border-radius: 0px;\n            cursor: pointer;\n            user-select: none;\n            vertical-align: middle;\n            appearance: none;\n            text-decoration: none;\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 500;\n            font-size: 14px;\n            line-height: 1.25;\n            letter-spacing: 0.02857em;\n            max-width: 360px;\n            min-width: 90px;\n            position: relative;\n            min-height: 48px;\n            flex-shrink: 0;\n            padding: 12px 16px;\n            overflow: hidden;\n            white-space: normal;\n            text-align: center;\n            flex-direction: column;\n            color: rgba(0, 0, 0, 0.6);\n        }\n\n        .narrow-accordion .jupyter-widget-Accordion-child .jupyter-widget-Collapse-header{\n            padding: 5px;\n        }  \n\n        .mod-active {\n            color: rgb(25, 118, 210) !important;\n            border-bottom: 2px solid rgb(25, 118, 210) !important;\n        }\n\n        .narrow-tab button {\n            max-width: 15%;\n        }\n\n        .narrow-tab-fairness button {\n            max-width: 20%;\n        }\n        .narrow-tab-fs button {\n            max-width: 25%;\n        }\n\n        .overflow-fix .jupyter-widgets {\n            overflow: auto;\n        }\n\n        .inner-padding {\n            padding-top: 20px;\n        }\n\n        .fix-img img {\n            width: 100%;\n        }\n\n        .fix-img-half img {\n            width: 50%;\n        }\n\n        .flex-reverse-for-update-button {\n            flex-direction: row-reverse;\n            margin-right: 50px;\n        }\n\n        .data-summary-tab {\n            max-height: 400px;\n        }\n\n        .restricted_table_height {\n            max-height: 450px\n        }\n\n        .input-width-with-parent .widget-dropdown {\n            width: 100%;\n        }\n\n        .input-width-with-parent .widget-text {\n            width: 100%;\n        }\n\n        .no-margin {\n            margin: 0px;\n        }\n\n        .widget-hprogress label {\n            text-align: left;\n        }\n\n        .widget-dropdown label {\n            margin-right: 10px;\n        }\n\n        .form .widget-dropdown {\n            width: auto;\n        }\n\n        .form .widget-text {\n            width: auto;\n        }\n\n        .form p {\n            margin: 0px;\n        }\n\n        .widget-dropdown {\n            white-space: nowrap;\n            overflow: hidden;\n            -o-text-overflow: ellipsis;\n            -ms-text-overflow: ellipsis;\n            text-overflow: ellipsis;\n        }\n\n        .progress-train label {\n            margin-right: 10px;\n        }\n\n        select {\n            height: 97% !important;\n        }\n\n        .no-overflow {\n            overflow: none;\n        }\n\n        .split-btn button {\n            margin-bottom: 2px;\n        }\n\n        .register-btn {\n            width: 50%\n        }\n\n        .register-btn-colab {\n            width: 40%\n        }\n\n        .fix-config-from-overflow .jp-OutputArea-child {\n            display: inherit;\n        }\n        \n        .checkbox-center input[type=\"checkbox\"] {\n            vertical-align: middle;\n        }\n\n        input[type=\"checkbox\"] {\n            vertical-align: middle;\n        }\n\n        </style>\n        "
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       "value": "\n        <style>\n\n        .left-label {\n            width: 30%;\n        }\n\n        .card-panel {\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            padding: 24px;\n            margin: 0.5rem 0 1rem 0;\n            border-radius: 2px;\n            background-color: #fff;\n        }\n\n        .card {\n            position: relative;\n            margin: 0.5rem 0.1rem 1rem 0.2rem;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 24px;\n            color: white;\n        }\n\n        .card .card-title {\n            font-size: 24px;\n            font-weight: 300;\n        }\n\n        @media only screen and (min-width: 800px) {\n            /* For notebook: */\n            .card {width: 99% !important;}\n        }\n\n        @media only screen and (min-width: 1400px) {\n            /* For colab: */\n            .card {width: 99% !important;}\n        }\n\n        .blue-grey.darken-1 {\n            background-color: #546e7a !important;\n        }\n\n        .small-card {\n            position: relative;\n            margin: 0.5rem 0 1rem 0;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 0px 5px 0px 5px;\n            color: white;\n        }\n\n        .inner-card {\n            position: relative;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 20px;\n            color: white;\n            margin: 0px 1px 1px 1px;\n        }\n\n        .card-with-tab {\n            position: relative;\n            margin: 0.5rem 0.1rem 1rem 0.2rem;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 24px;\n            color: white;\n        }\n\n        .card-with-tab button {\n            width: auto;\n            padding: 12px 40px;\n        }\n\n        .list {\n            padding: 0px 10px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            margin: 0px 1px 60px 1px;\n        }\n\n        .list-item {\n            padding: 10px 0px;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .divider {\n            margin: 10px 0px;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .hr {\n            margin: 0;\n            -webkit-flex-shrink: 0;\n            -ms-flex-negative: 0;\n            flex-shrink: 0;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .notification {\n            transition: box-shadow 300ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n            border-radius: 4px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 500;\n            font-size: 12px;\n            line-height: 1.43;\n            letter-spacing: 0.01071em;\n            background-color: rgb(2, 136, 209);\n            display: flex;\n            padding: 6px 16px;\n            color: rgb(255, 255, 255);\n            margin-top: 10px;\n        }\n\n        .success {\n            background-color: #EDF7ED;\n            color: #1E4620;\n        }\n\n        .error {\n            background-color: #FDEDED;\n            color: #5F2120;\n        }\n\n        .info {\n            background-color: #E5F6FD;\n            color: #528298;\n        }\n\n        .warn {\n            background-color: #FFF4E5;\n            color: #663C00;\n        }\n\n        .margin-top-20 {\n            margin-top: 20px;\n        }\n\n        .hide {\n            display: none;\n        }\n\n        .pbtn {\n            display: inline-flex;\n            -webkit-box-align: center;\n            align-items: center;\n            -webkit-box-pack: center;\n            justify-content: center;\n            position: relative;\n            box-sizing: border-box;\n            -webkit-tap-highlight-color: transparent;\n            outline: 0px;\n            border: 0px;\n            margin: 0px;\n            cursor: pointer;\n            user-select: none;\n            vertical-align: middle;\n            appearance: none;\n            text-decoration: none;\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 550;\n            font-size: 12px;\n            line-height: 1.75;\n            letter-spacing: 0.02857em;\n            text-transform: uppercase;\n            min-width: 64px;\n            padding: 6px 16px;\n            border-radius: 4px;\n            transition: background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n            color: rgb(255, 255, 255);\n            background-color: #D71E28;\n            box-shadow: rgb(0 0 0 / 20%) 0px 3px 1px -2px, rgb(0 0 0 / 14%) 0px 2px 2px 0px, rgb(0 0 0 / 12%) 0px 1px 5px 0px;\n        }\n\n        .jp-OutputArea-output {\n            flex-grow: 1;\n            flex-shrink: 1;\n            margin: 0px;\n        }\n\n        .output_subarea {\n            padding: 0px !important;\n        }\n\n        .ptable, .pth, .ptd {\n            border: none;\n        }\n\n        .pth, .ptd {\n            max-width: 150px;\n            white-space: nowrap;\n            overflow: hidden;\n            -o-text-overflow: ellipsis;\n            -ms-text-overflow: ellipsis;\n            text-overflow: ellipsis;\n        }\n\n        .ptable {\n            display: table;\n            border-collapse: collapse;\n            border-spacing: 0;\n        }\n\n        .ptable.striped .ptr {\n            border-bottom: none;\n        }\n\n        .ptable.striped > .ptbody > .ptr:nth-child(odd) {\n            background-color: rgba(242, 242, 242, 0.5);\n        }\n\n        .ptable.striped > .ptbody > .ptr > .ptd {\n            border-radius: 0;\n        }\n\n        .ptable.highlight > .ptbody > .ptr {\n            -webkit-transition: background-color .25s ease;\n            transition: background-color .25s ease;\n        }\n\n        .ptable.highlight > .ptbody > .ptr:hover {\n            background-color: rgba(242, 242, 242, 0.5);\n        }\n\n        .ptable.centered .pthead .ptr .pth, .ptable.centered .ptbody .ptr .ptd {\n            text-align: center;\n        }\n\n        .ptr {\n            border-bottom: 1px solid rgba(0, 0, 0, 0.12);\n        }\n\n        .ptr.hightlight {\n            background-color: yellow !important;\n        }\n\n        .ptd, .pth {\n            padding: 0.2rem 0.6rem;\n            display: table-cell;\n            text-align: right;\n            vertical-align: middle;\n            border-radius: 2px;\n            position: relative;\n        }\n\n        .ptr.grey {\n            background-color: grey !important;\n        }\n\n        .ptr.strikeout .ptd:before {\n            content: \" \";\n            position: absolute;\n            top: 50%;\n            left: 0;\n            border-bottom: 1px solid #111;\n            width: 100%;\n        }\n\n  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          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\util\\_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    210\u001b[0m         kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[1;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\util\\_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[0;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[0;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[0;32m    330\u001b[0m     )\n\u001b[1;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    948\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 950\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:605\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    604\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 605\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m   1441\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1735\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m   1734\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1735\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1736\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1737\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1738\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1739\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1740\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1741\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1742\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1743\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1744\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1745\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\common.py:713\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    712\u001b[0m \u001b[38;5;66;03m# open URLs\u001b[39;00m\n\u001b[1;32m--> 713\u001b[0m ioargs \u001b[38;5;241m=\u001b[39m \u001b[43m_get_filepath_or_buffer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    714\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath_or_buf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    715\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    716\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    717\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    718\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    719\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    721\u001b[0m handle \u001b[38;5;241m=\u001b[39m ioargs\u001b[38;5;241m.\u001b[39mfilepath_or_buffer\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\common.py:363\u001b[0m, in \u001b[0;36m_get_filepath_or_buffer\u001b[1;34m(filepath_or_buffer, encoding, compression, mode, storage_options)\u001b[0m\n\u001b[0;32m    362\u001b[0m req_info \u001b[38;5;241m=\u001b[39m urllib\u001b[38;5;241m.\u001b[39mrequest\u001b[38;5;241m.\u001b[39mRequest(filepath_or_buffer, headers\u001b[38;5;241m=\u001b[39mstorage_options)\n\u001b[1;32m--> 363\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq_info\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m req:\n\u001b[0;32m    364\u001b[0m     content_encoding \u001b[38;5;241m=\u001b[39m req\u001b[38;5;241m.\u001b[39mheaders\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mContent-Encoding\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\common.py:265\u001b[0m, in \u001b[0;36murlopen\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    263\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01murllib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrequest\u001b[39;00m\n\u001b[1;32m--> 265\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43murllib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:222\u001b[0m, in \u001b[0;36murlopen\u001b[1;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[0;32m    221\u001b[0m     opener \u001b[38;5;241m=\u001b[39m _opener\n\u001b[1;32m--> 222\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mopener\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:531\u001b[0m, in \u001b[0;36mOpenerDirector.open\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m    530\u001b[0m     meth \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(processor, meth_name)\n\u001b[1;32m--> 531\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[43mmeth\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    533\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m response\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:640\u001b[0m, in \u001b[0;36mHTTPErrorProcessor.http_response\u001b[1;34m(self, request, response)\u001b[0m\n\u001b[0;32m    639\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;241m200\u001b[39m \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m code \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m300\u001b[39m):\n\u001b[1;32m--> 640\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merror\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    641\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mhttp\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhdrs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    643\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m response\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:563\u001b[0m, in \u001b[0;36mOpenerDirector.error\u001b[1;34m(self, proto, *args)\u001b[0m\n\u001b[0;32m    562\u001b[0m args \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28mdict\u001b[39m, proto, meth_name) \u001b[38;5;241m+\u001b[39m args\n\u001b[1;32m--> 563\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_chain\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    564\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:502\u001b[0m, in \u001b[0;36mOpenerDirector._call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m    501\u001b[0m func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(handler, meth_name)\n\u001b[1;32m--> 502\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    503\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:755\u001b[0m, in \u001b[0;36mHTTPRedirectHandler.http_error_302\u001b[1;34m(self, req, fp, code, msg, headers)\u001b[0m\n\u001b[0;32m    753\u001b[0m fp\u001b[38;5;241m.\u001b[39mclose()\n\u001b[1;32m--> 755\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnew\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:525\u001b[0m, in \u001b[0;36mOpenerDirector.open\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m    524\u001b[0m sys\u001b[38;5;241m.\u001b[39maudit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124murllib.Request\u001b[39m\u001b[38;5;124m'\u001b[39m, req\u001b[38;5;241m.\u001b[39mfull_url, req\u001b[38;5;241m.\u001b[39mdata, req\u001b[38;5;241m.\u001b[39mheaders, req\u001b[38;5;241m.\u001b[39mget_method())\n\u001b[1;32m--> 525\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    527\u001b[0m \u001b[38;5;66;03m# post-process response\u001b[39;00m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:542\u001b[0m, in \u001b[0;36mOpenerDirector._open\u001b[1;34m(self, req, data)\u001b[0m\n\u001b[0;32m    541\u001b[0m protocol \u001b[38;5;241m=\u001b[39m req\u001b[38;5;241m.\u001b[39mtype\n\u001b[1;32m--> 542\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_chain\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_open\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprotocol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprotocol\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\n\u001b[0;32m    543\u001b[0m \u001b[43m                          \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m_open\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    544\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:502\u001b[0m, in \u001b[0;36mOpenerDirector._call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m    501\u001b[0m func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(handler, meth_name)\n\u001b[1;32m--> 502\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    503\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:1397\u001b[0m, in \u001b[0;36mHTTPSHandler.https_open\u001b[1;34m(self, req)\u001b[0m\n\u001b[0;32m   1396\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhttps_open\u001b[39m(\u001b[38;5;28mself\u001b[39m, req):\n\u001b[1;32m-> 1397\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhttp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mHTTPSConnection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1398\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcontext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_context\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_hostname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_hostname\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:1358\u001b[0m, in \u001b[0;36mAbstractHTTPHandler.do_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1357\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m URLError(err)\n\u001b[1;32m-> 1358\u001b[0m     r \u001b[38;5;241m=\u001b[39m \u001b[43mh\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1359\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:1347\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1346\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1347\u001b[0m     \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbegin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1348\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:307\u001b[0m, in \u001b[0;36mHTTPResponse.begin\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    306\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m--> 307\u001b[0m     version, status, reason \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_read_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    308\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m status \u001b[38;5;241m!=\u001b[39m CONTINUE:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:268\u001b[0m, in \u001b[0;36mHTTPResponse._read_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    267\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_read_status\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m--> 268\u001b[0m     line \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreadline\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_MAXLINE\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miso-8859-1\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    269\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(line) \u001b[38;5;241m>\u001b[39m _MAXLINE:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\socket.py:669\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m    668\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 669\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecv_into\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    670\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\ssl.py:1241\u001b[0m, in \u001b[0;36mSSLSocket.recv_into\u001b[1;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[0;32m   1238\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   1239\u001b[0m           \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[0;32m   1240\u001b[0m           \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m)\n\u001b[1;32m-> 1241\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnbytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1242\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\ssl.py:1099\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[1;34m(self, len, buffer)\u001b[0m\n\u001b[0;32m   1098\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m buffer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1099\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sslobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1100\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
          "\u001b[1;31mConnectionAbortedError\u001b[0m: [WinError 10053] 你的主机中的软件中止了一个已建立的连接。",
          "\nDuring handling of the above exception, another exception occurred:\n",
          "\u001b[1;31mgaierror\u001b[0m                                  Traceback (most recent call last)",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:1354\u001b[0m, in \u001b[0;36mAbstractHTTPHandler.do_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1353\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1354\u001b[0m     \u001b[43mh\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mselector\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1355\u001b[0m \u001b[43m              \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhas_header\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mTransfer-encoding\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1356\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err: \u001b[38;5;66;03m# timeout error\u001b[39;00m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:1255\u001b[0m, in \u001b[0;36mHTTPConnection.request\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1254\u001b[0m \u001b[38;5;124;03m\"\"\"Send a complete request to the server.\"\"\"\u001b[39;00m\n\u001b[1;32m-> 1255\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:1301\u001b[0m, in \u001b[0;36mHTTPConnection._send_request\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1300\u001b[0m     body \u001b[38;5;241m=\u001b[39m _encode(body, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbody\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m-> 1301\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mendheaders\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencode_chunked\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:1250\u001b[0m, in \u001b[0;36mHTTPConnection.endheaders\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1249\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m CannotSendHeader()\n\u001b[1;32m-> 1250\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_output\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessage_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencode_chunked\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:1010\u001b[0m, in \u001b[0;36mHTTPConnection._send_output\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1009\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_buffer[:]\n\u001b[1;32m-> 1010\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1012\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m message_body \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1013\u001b[0m \n\u001b[0;32m   1014\u001b[0m     \u001b[38;5;66;03m# create a consistent interface to message_body\u001b[39;00m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:950\u001b[0m, in \u001b[0;36mHTTPConnection.send\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    949\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_open:\n\u001b[1;32m--> 950\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    951\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:1417\u001b[0m, in \u001b[0;36mHTTPSConnection.connect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1415\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnect to a host on a given (SSL) port.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1417\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1419\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tunnel_host:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:921\u001b[0m, in \u001b[0;36mHTTPConnection.connect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    920\u001b[0m \u001b[38;5;124;03m\"\"\"Connect to the host and port specified in __init__.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 921\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_connection\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    922\u001b[0m \u001b[43m    \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mport\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msource_address\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    923\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock\u001b[38;5;241m.\u001b[39msetsockopt(socket\u001b[38;5;241m.\u001b[39mIPPROTO_TCP, socket\u001b[38;5;241m.\u001b[39mTCP_NODELAY, \u001b[38;5;241m1\u001b[39m)\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\socket.py:787\u001b[0m, in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address)\u001b[0m\n\u001b[0;32m    786\u001b[0m err \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m--> 787\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m \u001b[43mgetaddrinfo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mport\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mSOCK_STREAM\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m    788\u001b[0m     af, socktype, proto, canonname, sa \u001b[38;5;241m=\u001b[39m res\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\socket.py:918\u001b[0m, in \u001b[0;36mgetaddrinfo\u001b[1;34m(host, port, family, type, proto, flags)\u001b[0m\n\u001b[0;32m    917\u001b[0m addrlist \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m--> 918\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m \u001b[43m_socket\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetaddrinfo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mport\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfamily\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproto\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflags\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m    919\u001b[0m     af, socktype, proto, canonname, sa \u001b[38;5;241m=\u001b[39m res\n",
          "\u001b[1;31mgaierror\u001b[0m: [Errno 11001] getaddrinfo failed",
          "\nDuring handling of the above exception, another exception occurred:\n",
          "\u001b[1;31mURLError\u001b[0m                                  Traceback (most recent call last)",
          "File \u001b[1;32mpiml\\dashboard\\data_panel.py:111\u001b[0m, in \u001b[0;36mpiml.dashboard.data_panel.DataLoaderPanel.data_loader_panel.data_load\u001b[1;34m()\u001b[0m\n",
          "File \u001b[1;32mpiml\\data\\data_loader.py:129\u001b[0m, in \u001b[0;36mpiml.data.data_loader.load_online_data\u001b[1;34m()\u001b[0m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\util\\_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    209\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    210\u001b[0m         kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[1;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\util\\_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[0;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[0;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[0;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[0;32m    330\u001b[0m     )\n\u001b[1;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    935\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m    936\u001b[0m     dialect,\n\u001b[0;32m    937\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    946\u001b[0m     defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[0;32m    947\u001b[0m )\n\u001b[0;32m    948\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 950\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:605\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    602\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m    604\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 605\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m    608\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m   1439\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m   1441\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1735\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m   1733\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m   1734\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1735\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1736\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1737\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1738\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1739\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1740\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1741\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1742\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1743\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1744\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1745\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1746\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\common.py:713\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    710\u001b[0m     codecs\u001b[38;5;241m.\u001b[39mlookup_error(errors)\n\u001b[0;32m    712\u001b[0m \u001b[38;5;66;03m# open URLs\u001b[39;00m\n\u001b[1;32m--> 713\u001b[0m ioargs \u001b[38;5;241m=\u001b[39m \u001b[43m_get_filepath_or_buffer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    714\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath_or_buf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    715\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    716\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    717\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    718\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    719\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    721\u001b[0m handle \u001b[38;5;241m=\u001b[39m ioargs\u001b[38;5;241m.\u001b[39mfilepath_or_buffer\n\u001b[0;32m    722\u001b[0m handles: \u001b[38;5;28mlist\u001b[39m[BaseBuffer]\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\common.py:363\u001b[0m, in \u001b[0;36m_get_filepath_or_buffer\u001b[1;34m(filepath_or_buffer, encoding, compression, mode, storage_options)\u001b[0m\n\u001b[0;32m    361\u001b[0m \u001b[38;5;66;03m# assuming storage_options is to be interpreted as headers\u001b[39;00m\n\u001b[0;32m    362\u001b[0m req_info \u001b[38;5;241m=\u001b[39m urllib\u001b[38;5;241m.\u001b[39mrequest\u001b[38;5;241m.\u001b[39mRequest(filepath_or_buffer, headers\u001b[38;5;241m=\u001b[39mstorage_options)\n\u001b[1;32m--> 363\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq_info\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m req:\n\u001b[0;32m    364\u001b[0m     content_encoding \u001b[38;5;241m=\u001b[39m req\u001b[38;5;241m.\u001b[39mheaders\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mContent-Encoding\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m    365\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m content_encoding \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgzip\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    366\u001b[0m         \u001b[38;5;66;03m# Override compression based on Content-Encoding header\u001b[39;00m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\common.py:265\u001b[0m, in \u001b[0;36murlopen\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    259\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    260\u001b[0m \u001b[38;5;124;03mLazy-import wrapper for stdlib urlopen, as that imports a big chunk of\u001b[39;00m\n\u001b[0;32m    261\u001b[0m \u001b[38;5;124;03mthe stdlib.\u001b[39;00m\n\u001b[0;32m    262\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    263\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01murllib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrequest\u001b[39;00m\n\u001b[1;32m--> 265\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43murllib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:222\u001b[0m, in \u001b[0;36murlopen\u001b[1;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[0;32m    220\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    221\u001b[0m     opener \u001b[38;5;241m=\u001b[39m _opener\n\u001b[1;32m--> 222\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mopener\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:525\u001b[0m, in \u001b[0;36mOpenerDirector.open\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m    522\u001b[0m     req \u001b[38;5;241m=\u001b[39m meth(req)\n\u001b[0;32m    524\u001b[0m sys\u001b[38;5;241m.\u001b[39maudit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124murllib.Request\u001b[39m\u001b[38;5;124m'\u001b[39m, req\u001b[38;5;241m.\u001b[39mfull_url, req\u001b[38;5;241m.\u001b[39mdata, req\u001b[38;5;241m.\u001b[39mheaders, req\u001b[38;5;241m.\u001b[39mget_method())\n\u001b[1;32m--> 525\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    527\u001b[0m \u001b[38;5;66;03m# post-process response\u001b[39;00m\n\u001b[0;32m    528\u001b[0m meth_name \u001b[38;5;241m=\u001b[39m protocol\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_response\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:542\u001b[0m, in \u001b[0;36mOpenerDirector._open\u001b[1;34m(self, req, data)\u001b[0m\n\u001b[0;32m    539\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[0;32m    541\u001b[0m protocol \u001b[38;5;241m=\u001b[39m req\u001b[38;5;241m.\u001b[39mtype\n\u001b[1;32m--> 542\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_chain\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_open\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprotocol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprotocol\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\n\u001b[0;32m    543\u001b[0m \u001b[43m                          \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m_open\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    544\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result:\n\u001b[0;32m    545\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:502\u001b[0m, in \u001b[0;36mOpenerDirector._call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m    500\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m handler \u001b[38;5;129;01min\u001b[39;00m handlers:\n\u001b[0;32m    501\u001b[0m     func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(handler, meth_name)\n\u001b[1;32m--> 502\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    503\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    504\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:1397\u001b[0m, in \u001b[0;36mHTTPSHandler.https_open\u001b[1;34m(self, req)\u001b[0m\n\u001b[0;32m   1396\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhttps_open\u001b[39m(\u001b[38;5;28mself\u001b[39m, req):\n\u001b[1;32m-> 1397\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhttp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mHTTPSConnection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1398\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcontext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_context\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_hostname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_hostname\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:1357\u001b[0m, in \u001b[0;36mAbstractHTTPHandler.do_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1354\u001b[0m         h\u001b[38;5;241m.\u001b[39mrequest(req\u001b[38;5;241m.\u001b[39mget_method(), req\u001b[38;5;241m.\u001b[39mselector, req\u001b[38;5;241m.\u001b[39mdata, headers,\n\u001b[0;32m   1355\u001b[0m                   encode_chunked\u001b[38;5;241m=\u001b[39mreq\u001b[38;5;241m.\u001b[39mhas_header(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTransfer-encoding\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[0;32m   1356\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err: \u001b[38;5;66;03m# timeout error\u001b[39;00m\n\u001b[1;32m-> 1357\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m URLError(err)\n\u001b[0;32m   1358\u001b[0m     r \u001b[38;5;241m=\u001b[39m h\u001b[38;5;241m.\u001b[39mgetresponse()\n\u001b[0;32m   1359\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n",
          "\u001b[1;31mURLError\u001b[0m: <urlopen error [Errno 11001] getaddrinfo failed>"
         ]
        },
        {
         "ename": "URLError",
         "evalue": "<urlopen error [Errno 11001] getaddrinfo failed>",
         "output_type": "error",
         "traceback": [
          "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[1;31mgaierror\u001b[0m                                  Traceback (most recent call last)",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:1354\u001b[0m, in \u001b[0;36mAbstractHTTPHandler.do_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1353\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1354\u001b[0m     \u001b[43mh\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mselector\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1355\u001b[0m \u001b[43m              \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhas_header\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mTransfer-encoding\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1356\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err: \u001b[38;5;66;03m# timeout error\u001b[39;00m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:1255\u001b[0m, in \u001b[0;36mHTTPConnection.request\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1254\u001b[0m \u001b[38;5;124;03m\"\"\"Send a complete request to the server.\"\"\"\u001b[39;00m\n\u001b[1;32m-> 1255\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:1301\u001b[0m, in \u001b[0;36mHTTPConnection._send_request\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1300\u001b[0m     body \u001b[38;5;241m=\u001b[39m _encode(body, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbody\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m-> 1301\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mendheaders\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencode_chunked\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:1250\u001b[0m, in \u001b[0;36mHTTPConnection.endheaders\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1249\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m CannotSendHeader()\n\u001b[1;32m-> 1250\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_output\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessage_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencode_chunked\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:1010\u001b[0m, in \u001b[0;36mHTTPConnection._send_output\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1009\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_buffer[:]\n\u001b[1;32m-> 1010\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1012\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m message_body \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1013\u001b[0m \n\u001b[0;32m   1014\u001b[0m     \u001b[38;5;66;03m# create a consistent interface to message_body\u001b[39;00m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:950\u001b[0m, in \u001b[0;36mHTTPConnection.send\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    949\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_open:\n\u001b[1;32m--> 950\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    951\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:1417\u001b[0m, in \u001b[0;36mHTTPSConnection.connect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1415\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnect to a host on a given (SSL) port.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1417\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1419\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tunnel_host:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:921\u001b[0m, in \u001b[0;36mHTTPConnection.connect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    920\u001b[0m \u001b[38;5;124;03m\"\"\"Connect to the host and port specified in __init__.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 921\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_connection\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    922\u001b[0m \u001b[43m    \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mport\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msource_address\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    923\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock\u001b[38;5;241m.\u001b[39msetsockopt(socket\u001b[38;5;241m.\u001b[39mIPPROTO_TCP, socket\u001b[38;5;241m.\u001b[39mTCP_NODELAY, \u001b[38;5;241m1\u001b[39m)\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\socket.py:787\u001b[0m, in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address)\u001b[0m\n\u001b[0;32m    786\u001b[0m err \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m--> 787\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m \u001b[43mgetaddrinfo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mport\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mSOCK_STREAM\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m    788\u001b[0m     af, socktype, proto, canonname, sa \u001b[38;5;241m=\u001b[39m res\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\socket.py:918\u001b[0m, in \u001b[0;36mgetaddrinfo\u001b[1;34m(host, port, family, type, proto, flags)\u001b[0m\n\u001b[0;32m    917\u001b[0m addrlist \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m--> 918\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m \u001b[43m_socket\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetaddrinfo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mport\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfamily\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproto\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflags\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m    919\u001b[0m     af, socktype, proto, canonname, sa \u001b[38;5;241m=\u001b[39m res\n",
          "\u001b[1;31mgaierror\u001b[0m: [Errno 11004] getaddrinfo failed",
          "\nDuring handling of the above exception, another exception occurred:\n",
          "\u001b[1;31mURLError\u001b[0m                                  Traceback (most recent call last)",
          "File \u001b[1;32mpiml\\dashboard\\data_panel.py:108\u001b[0m, in \u001b[0;36mpiml.dashboard.data_panel.DataLoaderPanel.data_loader_panel.data_load\u001b[1;34m()\u001b[0m\n",
          "File \u001b[1;32mpiml\\data\\data_loader.py:129\u001b[0m, in \u001b[0;36mpiml.data.data_loader.load_online_data\u001b[1;34m()\u001b[0m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\util\\_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    210\u001b[0m         kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[1;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\util\\_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[0;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[0;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[0;32m    330\u001b[0m     )\n\u001b[1;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    948\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 950\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:605\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    604\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 605\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m   1441\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1735\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m   1734\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1735\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1736\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1737\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1738\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1739\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1740\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1741\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1742\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1743\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1744\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1745\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\common.py:713\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    712\u001b[0m \u001b[38;5;66;03m# open URLs\u001b[39;00m\n\u001b[1;32m--> 713\u001b[0m ioargs \u001b[38;5;241m=\u001b[39m \u001b[43m_get_filepath_or_buffer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    714\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath_or_buf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    715\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    716\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    717\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    718\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    719\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    721\u001b[0m handle \u001b[38;5;241m=\u001b[39m ioargs\u001b[38;5;241m.\u001b[39mfilepath_or_buffer\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\common.py:363\u001b[0m, in \u001b[0;36m_get_filepath_or_buffer\u001b[1;34m(filepath_or_buffer, encoding, compression, mode, storage_options)\u001b[0m\n\u001b[0;32m    362\u001b[0m req_info \u001b[38;5;241m=\u001b[39m urllib\u001b[38;5;241m.\u001b[39mrequest\u001b[38;5;241m.\u001b[39mRequest(filepath_or_buffer, headers\u001b[38;5;241m=\u001b[39mstorage_options)\n\u001b[1;32m--> 363\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq_info\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m req:\n\u001b[0;32m    364\u001b[0m     content_encoding \u001b[38;5;241m=\u001b[39m req\u001b[38;5;241m.\u001b[39mheaders\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mContent-Encoding\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\common.py:265\u001b[0m, in \u001b[0;36murlopen\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    263\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01murllib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrequest\u001b[39;00m\n\u001b[1;32m--> 265\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43murllib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:222\u001b[0m, in \u001b[0;36murlopen\u001b[1;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[0;32m    221\u001b[0m     opener \u001b[38;5;241m=\u001b[39m _opener\n\u001b[1;32m--> 222\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mopener\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:531\u001b[0m, in \u001b[0;36mOpenerDirector.open\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m    530\u001b[0m     meth \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(processor, meth_name)\n\u001b[1;32m--> 531\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[43mmeth\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    533\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m response\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:640\u001b[0m, in \u001b[0;36mHTTPErrorProcessor.http_response\u001b[1;34m(self, request, response)\u001b[0m\n\u001b[0;32m    639\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;241m200\u001b[39m \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m code \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m300\u001b[39m):\n\u001b[1;32m--> 640\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merror\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    641\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mhttp\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhdrs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    643\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m response\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:563\u001b[0m, in \u001b[0;36mOpenerDirector.error\u001b[1;34m(self, proto, *args)\u001b[0m\n\u001b[0;32m    562\u001b[0m args \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28mdict\u001b[39m, proto, meth_name) \u001b[38;5;241m+\u001b[39m args\n\u001b[1;32m--> 563\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_chain\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    564\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:502\u001b[0m, in \u001b[0;36mOpenerDirector._call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m    501\u001b[0m func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(handler, meth_name)\n\u001b[1;32m--> 502\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    503\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:755\u001b[0m, in \u001b[0;36mHTTPRedirectHandler.http_error_302\u001b[1;34m(self, req, fp, code, msg, headers)\u001b[0m\n\u001b[0;32m    753\u001b[0m fp\u001b[38;5;241m.\u001b[39mclose()\n\u001b[1;32m--> 755\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnew\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:531\u001b[0m, in \u001b[0;36mOpenerDirector.open\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m    530\u001b[0m     meth \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(processor, meth_name)\n\u001b[1;32m--> 531\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[43mmeth\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    533\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m response\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:640\u001b[0m, in \u001b[0;36mHTTPErrorProcessor.http_response\u001b[1;34m(self, request, response)\u001b[0m\n\u001b[0;32m    639\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;241m200\u001b[39m \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m code \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m300\u001b[39m):\n\u001b[1;32m--> 640\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merror\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    641\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mhttp\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhdrs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    643\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m response\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:563\u001b[0m, in \u001b[0;36mOpenerDirector.error\u001b[1;34m(self, proto, *args)\u001b[0m\n\u001b[0;32m    562\u001b[0m args \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28mdict\u001b[39m, proto, meth_name) \u001b[38;5;241m+\u001b[39m args\n\u001b[1;32m--> 563\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_chain\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    564\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:502\u001b[0m, in \u001b[0;36mOpenerDirector._call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m    501\u001b[0m func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(handler, meth_name)\n\u001b[1;32m--> 502\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    503\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:755\u001b[0m, in \u001b[0;36mHTTPRedirectHandler.http_error_302\u001b[1;34m(self, req, fp, code, msg, headers)\u001b[0m\n\u001b[0;32m    753\u001b[0m fp\u001b[38;5;241m.\u001b[39mclose()\n\u001b[1;32m--> 755\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnew\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:525\u001b[0m, in \u001b[0;36mOpenerDirector.open\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m    524\u001b[0m sys\u001b[38;5;241m.\u001b[39maudit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124murllib.Request\u001b[39m\u001b[38;5;124m'\u001b[39m, req\u001b[38;5;241m.\u001b[39mfull_url, req\u001b[38;5;241m.\u001b[39mdata, req\u001b[38;5;241m.\u001b[39mheaders, req\u001b[38;5;241m.\u001b[39mget_method())\n\u001b[1;32m--> 525\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    527\u001b[0m \u001b[38;5;66;03m# post-process response\u001b[39;00m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:542\u001b[0m, in \u001b[0;36mOpenerDirector._open\u001b[1;34m(self, req, data)\u001b[0m\n\u001b[0;32m    541\u001b[0m protocol \u001b[38;5;241m=\u001b[39m req\u001b[38;5;241m.\u001b[39mtype\n\u001b[1;32m--> 542\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_chain\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_open\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprotocol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprotocol\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\n\u001b[0;32m    543\u001b[0m \u001b[43m                          \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m_open\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    544\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:502\u001b[0m, in \u001b[0;36mOpenerDirector._call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m    501\u001b[0m func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(handler, meth_name)\n\u001b[1;32m--> 502\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    503\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:1397\u001b[0m, in \u001b[0;36mHTTPSHandler.https_open\u001b[1;34m(self, req)\u001b[0m\n\u001b[0;32m   1396\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhttps_open\u001b[39m(\u001b[38;5;28mself\u001b[39m, req):\n\u001b[1;32m-> 1397\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhttp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mHTTPSConnection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1398\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcontext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_context\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_hostname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_hostname\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:1357\u001b[0m, in \u001b[0;36mAbstractHTTPHandler.do_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1356\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err: \u001b[38;5;66;03m# timeout error\u001b[39;00m\n\u001b[1;32m-> 1357\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m URLError(err)\n\u001b[0;32m   1358\u001b[0m r \u001b[38;5;241m=\u001b[39m h\u001b[38;5;241m.\u001b[39mgetresponse()\n",
          "\u001b[1;31mURLError\u001b[0m: <urlopen error [Errno 11004] getaddrinfo failed>",
          "\nDuring handling of the above exception, another exception occurred:\n",
          "\u001b[1;31mgaierror\u001b[0m                                  Traceback (most recent call last)",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:1354\u001b[0m, in \u001b[0;36mAbstractHTTPHandler.do_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1353\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1354\u001b[0m     \u001b[43mh\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mselector\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1355\u001b[0m \u001b[43m              \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhas_header\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mTransfer-encoding\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1356\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err: \u001b[38;5;66;03m# timeout error\u001b[39;00m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:1255\u001b[0m, in \u001b[0;36mHTTPConnection.request\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1254\u001b[0m \u001b[38;5;124;03m\"\"\"Send a complete request to the server.\"\"\"\u001b[39;00m\n\u001b[1;32m-> 1255\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:1301\u001b[0m, in \u001b[0;36mHTTPConnection._send_request\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1300\u001b[0m     body \u001b[38;5;241m=\u001b[39m _encode(body, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbody\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m-> 1301\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mendheaders\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencode_chunked\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:1250\u001b[0m, in \u001b[0;36mHTTPConnection.endheaders\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1249\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m CannotSendHeader()\n\u001b[1;32m-> 1250\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_output\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessage_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencode_chunked\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:1010\u001b[0m, in \u001b[0;36mHTTPConnection._send_output\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1009\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_buffer[:]\n\u001b[1;32m-> 1010\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1012\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m message_body \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1013\u001b[0m \n\u001b[0;32m   1014\u001b[0m     \u001b[38;5;66;03m# create a consistent interface to message_body\u001b[39;00m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:950\u001b[0m, in \u001b[0;36mHTTPConnection.send\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    949\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_open:\n\u001b[1;32m--> 950\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    951\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:1417\u001b[0m, in \u001b[0;36mHTTPSConnection.connect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1415\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnect to a host on a given (SSL) port.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1417\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1419\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tunnel_host:\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\http\\client.py:921\u001b[0m, in \u001b[0;36mHTTPConnection.connect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    920\u001b[0m \u001b[38;5;124;03m\"\"\"Connect to the host and port specified in __init__.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 921\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_connection\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    922\u001b[0m \u001b[43m    \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mport\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msource_address\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    923\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock\u001b[38;5;241m.\u001b[39msetsockopt(socket\u001b[38;5;241m.\u001b[39mIPPROTO_TCP, socket\u001b[38;5;241m.\u001b[39mTCP_NODELAY, \u001b[38;5;241m1\u001b[39m)\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\socket.py:787\u001b[0m, in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address)\u001b[0m\n\u001b[0;32m    786\u001b[0m err \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m--> 787\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m \u001b[43mgetaddrinfo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mport\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mSOCK_STREAM\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m    788\u001b[0m     af, socktype, proto, canonname, sa \u001b[38;5;241m=\u001b[39m res\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\socket.py:918\u001b[0m, in \u001b[0;36mgetaddrinfo\u001b[1;34m(host, port, family, type, proto, flags)\u001b[0m\n\u001b[0;32m    917\u001b[0m addrlist \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m--> 918\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m \u001b[43m_socket\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetaddrinfo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mport\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfamily\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproto\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflags\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m    919\u001b[0m     af, socktype, proto, canonname, sa \u001b[38;5;241m=\u001b[39m res\n",
          "\u001b[1;31mgaierror\u001b[0m: [Errno 11001] getaddrinfo failed",
          "\nDuring handling of the above exception, another exception occurred:\n",
          "\u001b[1;31mURLError\u001b[0m                                  Traceback (most recent call last)",
          "File \u001b[1;32mpiml\\dashboard\\data_panel.py:111\u001b[0m, in \u001b[0;36mpiml.dashboard.data_panel.DataLoaderPanel.data_loader_panel.data_load\u001b[1;34m()\u001b[0m\n",
          "File \u001b[1;32mpiml\\data\\data_loader.py:129\u001b[0m, in \u001b[0;36mpiml.data.data_loader.load_online_data\u001b[1;34m()\u001b[0m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\util\\_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    209\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    210\u001b[0m         kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[1;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\util\\_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[0;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[0;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[0;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[0;32m    330\u001b[0m     )\n\u001b[1;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    935\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m    936\u001b[0m     dialect,\n\u001b[0;32m    937\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    946\u001b[0m     defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[0;32m    947\u001b[0m )\n\u001b[0;32m    948\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 950\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:605\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    602\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m    604\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 605\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m    608\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m   1439\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m   1441\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1735\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m   1733\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m   1734\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1735\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1736\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1737\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1738\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1739\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1740\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1741\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1742\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1743\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1744\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1745\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1746\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\common.py:713\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    710\u001b[0m     codecs\u001b[38;5;241m.\u001b[39mlookup_error(errors)\n\u001b[0;32m    712\u001b[0m \u001b[38;5;66;03m# open URLs\u001b[39;00m\n\u001b[1;32m--> 713\u001b[0m ioargs \u001b[38;5;241m=\u001b[39m \u001b[43m_get_filepath_or_buffer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    714\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath_or_buf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    715\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    716\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    717\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    718\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    719\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    721\u001b[0m handle \u001b[38;5;241m=\u001b[39m ioargs\u001b[38;5;241m.\u001b[39mfilepath_or_buffer\n\u001b[0;32m    722\u001b[0m handles: \u001b[38;5;28mlist\u001b[39m[BaseBuffer]\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\common.py:363\u001b[0m, in \u001b[0;36m_get_filepath_or_buffer\u001b[1;34m(filepath_or_buffer, encoding, compression, mode, storage_options)\u001b[0m\n\u001b[0;32m    361\u001b[0m \u001b[38;5;66;03m# assuming storage_options is to be interpreted as headers\u001b[39;00m\n\u001b[0;32m    362\u001b[0m req_info \u001b[38;5;241m=\u001b[39m urllib\u001b[38;5;241m.\u001b[39mrequest\u001b[38;5;241m.\u001b[39mRequest(filepath_or_buffer, headers\u001b[38;5;241m=\u001b[39mstorage_options)\n\u001b[1;32m--> 363\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq_info\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m req:\n\u001b[0;32m    364\u001b[0m     content_encoding \u001b[38;5;241m=\u001b[39m req\u001b[38;5;241m.\u001b[39mheaders\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mContent-Encoding\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m    365\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m content_encoding \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgzip\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    366\u001b[0m         \u001b[38;5;66;03m# Override compression based on Content-Encoding header\u001b[39;00m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\io\\common.py:265\u001b[0m, in \u001b[0;36murlopen\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    259\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    260\u001b[0m \u001b[38;5;124;03mLazy-import wrapper for stdlib urlopen, as that imports a big chunk of\u001b[39;00m\n\u001b[0;32m    261\u001b[0m \u001b[38;5;124;03mthe stdlib.\u001b[39;00m\n\u001b[0;32m    262\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    263\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01murllib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrequest\u001b[39;00m\n\u001b[1;32m--> 265\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43murllib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:222\u001b[0m, in \u001b[0;36murlopen\u001b[1;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[0;32m    220\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    221\u001b[0m     opener \u001b[38;5;241m=\u001b[39m _opener\n\u001b[1;32m--> 222\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mopener\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:525\u001b[0m, in \u001b[0;36mOpenerDirector.open\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m    522\u001b[0m     req \u001b[38;5;241m=\u001b[39m meth(req)\n\u001b[0;32m    524\u001b[0m sys\u001b[38;5;241m.\u001b[39maudit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124murllib.Request\u001b[39m\u001b[38;5;124m'\u001b[39m, req\u001b[38;5;241m.\u001b[39mfull_url, req\u001b[38;5;241m.\u001b[39mdata, req\u001b[38;5;241m.\u001b[39mheaders, req\u001b[38;5;241m.\u001b[39mget_method())\n\u001b[1;32m--> 525\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    527\u001b[0m \u001b[38;5;66;03m# post-process response\u001b[39;00m\n\u001b[0;32m    528\u001b[0m meth_name \u001b[38;5;241m=\u001b[39m protocol\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_response\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:542\u001b[0m, in \u001b[0;36mOpenerDirector._open\u001b[1;34m(self, req, data)\u001b[0m\n\u001b[0;32m    539\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[0;32m    541\u001b[0m protocol \u001b[38;5;241m=\u001b[39m req\u001b[38;5;241m.\u001b[39mtype\n\u001b[1;32m--> 542\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_chain\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_open\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprotocol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprotocol\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\n\u001b[0;32m    543\u001b[0m \u001b[43m                          \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m_open\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    544\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result:\n\u001b[0;32m    545\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:502\u001b[0m, in \u001b[0;36mOpenerDirector._call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m    500\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m handler \u001b[38;5;129;01min\u001b[39;00m handlers:\n\u001b[0;32m    501\u001b[0m     func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(handler, meth_name)\n\u001b[1;32m--> 502\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    503\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    504\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:1397\u001b[0m, in \u001b[0;36mHTTPSHandler.https_open\u001b[1;34m(self, req)\u001b[0m\n\u001b[0;32m   1396\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhttps_open\u001b[39m(\u001b[38;5;28mself\u001b[39m, req):\n\u001b[1;32m-> 1397\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhttp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mHTTPSConnection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1398\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcontext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_context\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_hostname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_hostname\u001b[49m\u001b[43m)\u001b[49m\n",
          "File \u001b[1;32mD:\\anaconda\\lib\\urllib\\request.py:1357\u001b[0m, in \u001b[0;36mAbstractHTTPHandler.do_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1354\u001b[0m         h\u001b[38;5;241m.\u001b[39mrequest(req\u001b[38;5;241m.\u001b[39mget_method(), req\u001b[38;5;241m.\u001b[39mselector, req\u001b[38;5;241m.\u001b[39mdata, headers,\n\u001b[0;32m   1355\u001b[0m                   encode_chunked\u001b[38;5;241m=\u001b[39mreq\u001b[38;5;241m.\u001b[39mhas_header(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTransfer-encoding\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[0;32m   1356\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err: \u001b[38;5;66;03m# timeout error\u001b[39;00m\n\u001b[1;32m-> 1357\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m URLError(err)\n\u001b[0;32m   1358\u001b[0m     r \u001b[38;5;241m=\u001b[39m h\u001b[38;5;241m.\u001b[39mgetresponse()\n\u001b[0;32m   1359\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n",
          "\u001b[1;31mURLError\u001b[0m: <urlopen error [Errno 11001] getaddrinfo failed>"
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\n",
          "text/plain": "<Figure size 864x324 with 4 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "76b4f5f756f84584a2ae919b838be605": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "width": "20%"
      }
     },
     "76b811c0fbe845c7b01b5b8beb97f0b0": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_55b17d94c4fc4d67b8722f955c99bfc6",
       "style": "IPY_MODEL_0ae1d2fac0ff495a8f2538da344d0db6",
       "value": "\n        <style>\n\n        .left-label {\n            width: 30%;\n        }\n\n        .card-panel {\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            padding: 24px;\n            margin: 0.5rem 0 1rem 0;\n            border-radius: 2px;\n            background-color: #fff;\n        }\n\n        .card {\n            position: relative;\n            margin: 0.5rem 0.1rem 1rem 0.2rem;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 24px;\n            color: white;\n        }\n\n        .card .card-title {\n            font-size: 24px;\n            font-weight: 300;\n        }\n\n        @media only screen and (min-width: 800px) {\n            /* For notebook: */\n            .card {width: 99% !important;}\n        }\n\n        @media only screen and (min-width: 1400px) {\n            /* For colab: */\n            .card {width: 99% !important;}\n        }\n\n        .blue-grey.darken-1 {\n            background-color: #546e7a !important;\n        }\n\n        .small-card {\n            position: relative;\n            margin: 0.5rem 0 1rem 0;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 0px 5px 0px 5px;\n            color: white;\n        }\n\n        .inner-card {\n            position: relative;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 20px;\n            color: white;\n            margin: 0px 1px 1px 1px;\n        }\n\n        .card-with-tab {\n            position: relative;\n            margin: 0.5rem 0.1rem 1rem 0.2rem;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 24px;\n            color: white;\n        }\n\n        .card-with-tab button {\n            width: auto;\n            padding: 12px 40px;\n        }\n\n        .list {\n            padding: 0px 10px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            margin: 0px 1px 60px 1px;\n        }\n\n        .list-item {\n            padding: 10px 0px;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .divider {\n            margin: 10px 0px;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .hr {\n            margin: 0;\n            -webkit-flex-shrink: 0;\n            -ms-flex-negative: 0;\n            flex-shrink: 0;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .notification {\n            transition: box-shadow 300ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n            border-radius: 4px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 500;\n            font-size: 12px;\n            line-height: 1.43;\n            letter-spacing: 0.01071em;\n            background-color: rgb(2, 136, 209);\n            display: flex;\n            padding: 6px 16px;\n            color: rgb(255, 255, 255);\n            margin-top: 10px;\n        }\n\n        .success {\n            background-color: #EDF7ED;\n            color: #1E4620;\n        }\n\n        .error {\n            background-color: #FDEDED;\n            color: #5F2120;\n        }\n\n        .info {\n            background-color: #E5F6FD;\n            color: #528298;\n        }\n\n        .warn {\n            background-color: #FFF4E5;\n            color: #663C00;\n        }\n\n        .margin-top-20 {\n            margin-top: 20px;\n        }\n\n        .hide {\n            display: none;\n        }\n\n        .pbtn {\n            display: inline-flex;\n            -webkit-box-align: center;\n            align-items: center;\n            -webkit-box-pack: center;\n            justify-content: center;\n            position: relative;\n            box-sizing: border-box;\n            -webkit-tap-highlight-color: transparent;\n            outline: 0px;\n            border: 0px;\n            margin: 0px;\n            cursor: pointer;\n            user-select: none;\n            vertical-align: middle;\n            appearance: none;\n            text-decoration: none;\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 550;\n            font-size: 12px;\n            line-height: 1.75;\n            letter-spacing: 0.02857em;\n            text-transform: uppercase;\n            min-width: 64px;\n            padding: 6px 16px;\n            border-radius: 4px;\n            transition: background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n            color: rgb(255, 255, 255);\n            background-color: #D71E28;\n            box-shadow: rgb(0 0 0 / 20%) 0px 3px 1px -2px, rgb(0 0 0 / 14%) 0px 2px 2px 0px, rgb(0 0 0 / 12%) 0px 1px 5px 0px;\n        }\n\n        .jp-OutputArea-output {\n            flex-grow: 1;\n            flex-shrink: 1;\n            margin: 0px;\n        }\n\n        .output_subarea {\n            padding: 0px !important;\n        }\n\n        .ptable, .pth, .ptd {\n            border: none;\n        }\n\n        .pth, .ptd {\n            max-width: 150px;\n            white-space: nowrap;\n            overflow: hidden;\n            -o-text-overflow: ellipsis;\n            -ms-text-overflow: ellipsis;\n            text-overflow: ellipsis;\n        }\n\n        .ptable {\n            display: table;\n            border-collapse: collapse;\n            border-spacing: 0;\n        }\n\n        .ptable.striped .ptr {\n            border-bottom: none;\n        }\n\n        .ptable.striped > .ptbody > .ptr:nth-child(odd) {\n            background-color: rgba(242, 242, 242, 0.5);\n        }\n\n        .ptable.striped > .ptbody > .ptr > .ptd {\n            border-radius: 0;\n        }\n\n        .ptable.highlight > .ptbody > .ptr {\n            -webkit-transition: background-color .25s ease;\n            transition: background-color .25s ease;\n        }\n\n        .ptable.highlight > .ptbody > .ptr:hover {\n            background-color: rgba(242, 242, 242, 0.5);\n        }\n\n        .ptable.centered .pthead .ptr .pth, .ptable.centered .ptbody .ptr .ptd {\n            text-align: center;\n        }\n\n        .ptr {\n            border-bottom: 1px solid rgba(0, 0, 0, 0.12);\n        }\n\n        .ptr.hightlight {\n            background-color: yellow !important;\n        }\n\n        .ptd, .pth {\n            padding: 0.2rem 0.6rem;\n            display: table-cell;\n            text-align: right;\n            vertical-align: middle;\n            border-radius: 2px;\n            position: relative;\n        }\n\n        .ptr.grey {\n            background-color: grey !important;\n        }\n\n        .ptr.strikeout .ptd:before {\n            content: \" \";\n            position: absolute;\n            top: 50%;\n            left: 0;\n            border-bottom: 1px solid #111;\n            width: 100%;\n        }\n\n        .widget-toggle-button {\n            display: inline-flex;\n            -webkit-box-align: center;\n            align-items: center;\n            -webkit-box-pack: center;\n            justify-content: center;\n            box-sizing: border-box;\n            -webkit-tap-highlight-color: transparent;\n            background-color: transparent !important;\n            outline: 0px;\n            border: 0px;\n            border-bottom: 2px solid #E4E4E4 !important;\n            box-shadow: none !important;\n            margin: 0px !important;\n            border-radius: 0px;\n            cursor: pointer;\n            user-select: none;\n            vertical-align: middle;\n            appearance: none;\n            text-decoration: none;\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 500;\n            font-size: 14px;\n            line-height: 1.25;\n            letter-spacing: 0.02857em;\n            max-width: 360px;\n            min-width: 90px;\n            position: relative;\n            min-height: 48px;\n            flex-shrink: 0;\n            padding: 12px 16px;\n            overflow: hidden;\n            white-space: normal;\n            text-align: center;\n            flex-direction: column;\n            color: rgba(0, 0, 0, 0.6);\n        }\n\n        .narrow-accordion .jupyter-widget-Accordion-child .jupyter-widget-Collapse-header{\n            padding: 5px;\n        }  \n\n        .mod-active {\n            color: rgb(25, 118, 210) !important;\n            border-bottom: 2px solid rgb(25, 118, 210) !important;\n        }\n\n        .narrow-tab button {\n            max-width: 15%;\n        }\n\n        .narrow-tab-fairness button {\n            max-width: 20%;\n        }\n        .narrow-tab-fs button {\n            max-width: 25%;\n        }\n\n        .overflow-fix .jupyter-widgets {\n            overflow: auto;\n        }\n\n        .inner-padding {\n            padding-top: 20px;\n        }\n\n        .fix-img img {\n            width: 100%;\n        }\n\n        .fix-img-half img {\n            width: 50%;\n        }\n\n        .flex-reverse-for-update-button {\n            flex-direction: row-reverse;\n            margin-right: 50px;\n        }\n\n        .data-summary-tab {\n            max-height: 400px;\n        }\n\n        .restricted_table_height {\n            max-height: 450px\n        }\n\n        .input-width-with-parent .widget-dropdown {\n            width: 100%;\n        }\n\n        .input-width-with-parent .widget-text {\n            width: 100%;\n        }\n\n        .no-margin {\n            margin: 0px;\n        }\n\n        .widget-hprogress label {\n            text-align: left;\n        }\n\n        .widget-dropdown label {\n            margin-right: 10px;\n        }\n\n        .form .widget-dropdown {\n            width: auto;\n        }\n\n        .form .widget-text {\n            width: auto;\n        }\n\n        .form p {\n            margin: 0px;\n        }\n\n        .widget-dropdown {\n            white-space: nowrap;\n            overflow: hidden;\n            -o-text-overflow: ellipsis;\n            -ms-text-overflow: ellipsis;\n            text-overflow: ellipsis;\n        }\n\n        .progress-train label {\n            margin-right: 10px;\n        }\n\n        select {\n            height: 97% !important;\n        }\n\n        .no-overflow {\n            overflow: none;\n        }\n\n        .split-btn button {\n            margin-bottom: 2px;\n        }\n\n        .register-btn {\n            width: 50%\n        }\n\n        .register-btn-colab {\n            width: 40%\n        }\n\n        .fix-config-from-overflow .jp-OutputArea-child {\n            display: inherit;\n        }\n        \n        .checkbox-center input[type=\"checkbox\"] {\n            vertical-align: middle;\n        }\n\n        input[type=\"checkbox\"] {\n            vertical-align: middle;\n        }\n\n        </style>\n        "
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       "width": "15%"
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     "76c18ee445664397afcc4dc182891ab0": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatRangeSliderModel",
      "state": {
       "_model_name": "FloatRangeSliderModel",
       "_view_name": "FloatRangeSliderView",
       "layout": "IPY_MODEL_f861ad1601bf471383b64d892f609420",
       "readout": false,
       "step": 0.1,
       "style": "IPY_MODEL_5f022427ef364819a085f174169d2d06",
       "value": [
        25,
        75
       ]
      }
     },
     "76c8b54157524e65932d996e65339aa5": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "ButtonModel",
      "state": {
       "_dom_classes": [
        "pbtn"
       ],
       "button_style": "danger",
       "description": "RUN",
       "layout": "IPY_MODEL_d99da86ea3304060921d33beafcb1f97",
       "style": "IPY_MODEL_771f8b50aca14b4f926b09d8f4270c94"
      }
     },
     "76cd405768214db8b2900df9653013d0": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
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     },
     "76d13c4073f54e48a9e57d4d0f08b245": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "width": "80%"
      }
     },
     "76ede81616384571815dd56588d29383": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "76eef157340e46ca9f1aa11023dc00e0": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "76f73c6e6b3d4852be26d643b060e09e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
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     "77073fab9dbe401891d7226c59d4179d": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_b30f8120119a4c579ba4f11f5de3c3da",
       "outputs": [
        {
         "data": {
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          "text/plain": "<Figure size 1200x450 with 2 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
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\n",
          "text/plain": "<Figure size 864x324 with 4 Axes>"
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          "text/html": "<style type=\"text/css\">\n</style>\n<table id=\"T_a561a\">\n  <thead>\n    <tr>\n      <th id=\"T_a561a_level0_col0\" class=\"col_heading level0 col0\" >Group Index</th>\n      <th id=\"T_a561a_level0_col1\" class=\"col_heading level0 col1\" >Group Category</th>\n      <th id=\"T_a561a_level0_col2\" class=\"col_heading level0 col2\" >Reference Group</th>\n      <th id=\"T_a561a_level0_col3\" class=\"col_heading level0 col3\" >Protected Group</th>\n      <th id=\"T_a561a_level0_col4\" class=\"col_heading level0 col4\" >Threshold</th>\n      <th id=\"T_a561a_level0_col5\" class=\"col_heading level0 col5\" >AIR</th>\n      <th id=\"T_a561a_level0_col6\" class=\"col_heading level0 col6\" >ACC</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_a561a_row0_col0\" class=\"data row0 col0\" >0</td>\n      <td id=\"T_a561a_row0_col1\" class=\"data row0 col1\" >Gender</td>\n      <td id=\"T_a561a_row0_col2\" class=\"data row0 col2\" >1.0</td>\n      <td id=\"T_a561a_row0_col3\" class=\"data row0 col3\" >0.0</td>\n      <td id=\"T_a561a_row0_col4\" class=\"data row0 col4\" >0.50</td>\n      <td id=\"T_a561a_row0_col5\" class=\"data row0 col5\" >0.768300</td>\n      <td id=\"T_a561a_row0_col6\" class=\"data row0 col6\" >0.700350</td>\n    </tr>\n    <tr>\n      <td id=\"T_a561a_row1_col0\" class=\"data row1 col0\" >1</td>\n      <td id=\"T_a561a_row1_col1\" class=\"data row1 col1\" >Race</td>\n      <td id=\"T_a561a_row1_col2\" class=\"data row1 col2\" >1.0</td>\n      <td id=\"T_a561a_row1_col3\" class=\"data row1 col3\" >0.0</td>\n      <td id=\"T_a561a_row1_col4\" class=\"data row1 col4\" >0.50</td>\n      <td id=\"T_a561a_row1_col5\" class=\"data row1 col5\" >0.618326</td>\n      <td id=\"T_a561a_row1_col6\" class=\"data row1 col6\" >0.700350</td>\n    </tr>\n  </tbody>\n</table>\n",
          "text/plain": "<pandas.io.formats.style.Styler at 0x2c76599a1c0>"
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          "text/plain": "<Figure size 1200x450 with 2 Axes>"
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        "IPY_MODEL_abf64c78f7334c8ab68ad0906e6b0812",
        "IPY_MODEL_7e23953723a5405b82d713c99f27ccc1",
        "IPY_MODEL_5d198332c5594d3cb70ff24ecbec7c3a",
        "IPY_MODEL_5d7389abc3dc4a468706c6e1b1425e6d",
        "IPY_MODEL_e6fba6fd37f148738a81e3009fd915e9",
        "IPY_MODEL_21cb771e6c3d40c2953e12fd40d6d725",
        "IPY_MODEL_873226ecf6294a4197a4fb98c6840e98",
        "IPY_MODEL_a1cbf88b6a0545638579a41c8e2d634a",
        "IPY_MODEL_1a756cea344e4d29bb4eac33f3d084b9"
       ],
       "layout": "IPY_MODEL_77ef1cd5f2634ac08b9183c108262184"
      }
     },
     "9efef55bd62e442b9f4a9e260a1fc457": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_385fa6a7c5b04e6e8fd0c7a147390ae9",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 600x600 with 2 Axes>"
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\n",
          "text/plain": "<Figure size 864x324 with 2 Axes>"
         },
         "metadata": {
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         },
         "output_type": "display_data"
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        {
         "data": {
          "text/html": "<style type=\"text/css\">\n</style>\n<table id=\"T_88c7a\">\n  <thead>\n    <tr>\n      <th id=\"T_88c7a_level0_col0\" class=\"col_heading level0 col0\" >Segment</th>\n      <th id=\"T_88c7a_level0_col1\" class=\"col_heading level0 col1\" >Lower Bound</th>\n      <th id=\"T_88c7a_level0_col2\" class=\"col_heading level0 col2\" >Upper Bound</th>\n      <th id=\"T_88c7a_level0_col3\" class=\"col_heading level0 col3\" >Group Index</th>\n      <th id=\"T_88c7a_level0_col4\" class=\"col_heading level0 col4\" >GLM_AIR</th>\n      <th id=\"T_88c7a_level0_col5\" class=\"col_heading level0 col5\" >XGB2_AIR</th>\n      <th id=\"T_88c7a_level0_col6\" class=\"col_heading level0 col6\" >XGB7_AIR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_88c7a_row0_col0\" class=\"data row0 col0\" >0</td>\n      <td id=\"T_88c7a_row0_col1\" class=\"data row0 col1\" >107620.670000</td>\n      <td id=\"T_88c7a_row0_col2\" class=\"data row0 col2\" >166338.870000</td>\n      <td id=\"T_88c7a_row0_col3\" class=\"data row0 col3\" >0</td>\n      <td id=\"T_88c7a_row0_col4\" class=\"data row0 col4\" >0.665243</td>\n      <td id=\"T_88c7a_row0_col5\" class=\"data row0 col5\" >0.332816</td>\n      <td id=\"T_88c7a_row0_col6\" class=\"data row0 col6\" >0.641605</td>\n    </tr>\n    <tr>\n      <td id=\"T_88c7a_row1_col0\" class=\"data row1 col0\" >0</td>\n      <td id=\"T_88c7a_row1_col1\" class=\"data row1 col1\" >107620.670000</td>\n      <td id=\"T_88c7a_row1_col2\" class=\"data row1 col2\" >166338.870000</td>\n      <td id=\"T_88c7a_row1_col3\" class=\"data row1 col3\" >1</td>\n      <td id=\"T_88c7a_row1_col4\" class=\"data row1 col4\" >0.949767</td>\n      <td id=\"T_88c7a_row1_col5\" class=\"data row1 col5\" >0.823692</td>\n      <td id=\"T_88c7a_row1_col6\" class=\"data row1 col6\" >0.876903</td>\n    </tr>\n    <tr>\n      <td id=\"T_88c7a_row2_col0\" class=\"data row2 col0\" >1</td>\n      <td id=\"T_88c7a_row2_col1\" class=\"data row2 col1\" >166342.440000</td>\n      <td id=\"T_88c7a_row2_col2\" class=\"data row2 col2\" >206850.600000</td>\n      <td id=\"T_88c7a_row2_col3\" class=\"data row2 col3\" >0</td>\n      <td id=\"T_88c7a_row2_col4\" class=\"data row2 col4\" >0.751820</td>\n      <td id=\"T_88c7a_row2_col5\" class=\"data row2 col5\" >0.680862</td>\n      <td id=\"T_88c7a_row2_col6\" class=\"data row2 col6\" >0.796834</td>\n    </tr>\n    <tr>\n      <td id=\"T_88c7a_row3_col0\" class=\"data row3 col0\" >1</td>\n      <td id=\"T_88c7a_row3_col1\" class=\"data row3 col1\" >166342.440000</td>\n      <td id=\"T_88c7a_row3_col2\" class=\"data row3 col2\" >206850.600000</td>\n      <td id=\"T_88c7a_row3_col3\" class=\"data row3 col3\" >1</td>\n      <td id=\"T_88c7a_row3_col4\" class=\"data row3 col4\" >0.972054</td>\n      <td id=\"T_88c7a_row3_col5\" class=\"data row3 col5\" >0.951986</td>\n      <td id=\"T_88c7a_row3_col6\" class=\"data row3 col6\" >0.952978</td>\n    </tr>\n    <tr>\n      <td id=\"T_88c7a_row4_col0\" class=\"data row4 col0\" >2</td>\n      <td id=\"T_88c7a_row4_col1\" class=\"data row4 col1\" >206853.100000</td>\n      <td id=\"T_88c7a_row4_col2\" class=\"data row4 col2\" >251710.040000</td>\n      <td id=\"T_88c7a_row4_col3\" class=\"data row4 col3\" >0</td>\n      <td id=\"T_88c7a_row4_col4\" class=\"data row4 col4\" >0.794783</td>\n      <td id=\"T_88c7a_row4_col5\" class=\"data row4 col5\" >0.878410</td>\n      <td id=\"T_88c7a_row4_col6\" class=\"data row4 col6\" >0.854068</td>\n    </tr>\n    <tr>\n      <td id=\"T_88c7a_row5_col0\" class=\"data row5 col0\" >2</td>\n      <td id=\"T_88c7a_row5_col1\" class=\"data row5 col1\" >206853.100000</td>\n      <td id=\"T_88c7a_row5_col2\" class=\"data row5 col2\" >251710.040000</td>\n      <td id=\"T_88c7a_row5_col3\" class=\"data row5 col3\" >1</td>\n      <td id=\"T_88c7a_row5_col4\" class=\"data row5 col4\" >1.034726</td>\n      <td id=\"T_88c7a_row5_col5\" class=\"data row5 col5\" >1.052155</td>\n      <td id=\"T_88c7a_row5_col6\" class=\"data row5 col6\" >0.997088</td>\n    </tr>\n    <tr>\n      <td id=\"T_88c7a_row6_col0\" class=\"data row6 col0\" >3</td>\n      <td id=\"T_88c7a_row6_col1\" class=\"data row6 col1\" >251719.830000</td>\n      <td id=\"T_88c7a_row6_col2\" class=\"data row6 col2\" >319081.470000</td>\n      <td id=\"T_88c7a_row6_col3\" class=\"data row6 col3\" >0</td>\n      <td id=\"T_88c7a_row6_col4\" class=\"data row6 col4\" >0.841141</td>\n      <td id=\"T_88c7a_row6_col5\" class=\"data row6 col5\" >0.859256</td>\n      <td id=\"T_88c7a_row6_col6\" class=\"data row6 col6\" >0.795912</td>\n    </tr>\n    <tr>\n      <td id=\"T_88c7a_row7_col0\" class=\"data row7 col0\" >3</td>\n      <td id=\"T_88c7a_row7_col1\" class=\"data row7 col1\" >251719.830000</td>\n      <td id=\"T_88c7a_row7_col2\" class=\"data row7 col2\" >319081.470000</td>\n      <td id=\"T_88c7a_row7_col3\" class=\"data row7 col3\" >1</td>\n      <td id=\"T_88c7a_row7_col4\" class=\"data row7 col4\" 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          "text/plain": "<Figure size 1200x450 with 4 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "text/html": "<style type=\"text/css\">\n</style>\n<table id=\"T_9eec5\">\n  <thead>\n    <tr>\n      <th id=\"T_9eec5_level0_col0\" class=\"col_heading level0 col0\" >Group Index</th>\n      <th id=\"T_9eec5_level0_col1\" class=\"col_heading level0 col1\" >Group Category</th>\n      <th id=\"T_9eec5_level0_col2\" class=\"col_heading level0 col2\" >Reference Group</th>\n      <th id=\"T_9eec5_level0_col3\" class=\"col_heading level0 col3\" >Protected Group</th>\n      <th id=\"T_9eec5_level0_col4\" class=\"col_heading level0 col4\" >Removed</th>\n      <th id=\"T_9eec5_level0_col5\" class=\"col_heading level0 col5\" >Removed Index</th>\n      <th id=\"T_9eec5_level0_col6\" class=\"col_heading level0 col6\" >AIR</th>\n      <th id=\"T_9eec5_level0_col7\" class=\"col_heading level0 col7\" >ACC</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_9eec5_row0_col0\" class=\"data row0 col0\" >0</td>\n      <td id=\"T_9eec5_row0_col1\" class=\"data row0 col1\" >Gender</td>\n      <td id=\"T_9eec5_row0_col2\" class=\"data row0 col2\" >1.0</td>\n      <td id=\"T_9eec5_row0_col3\" class=\"data row0 col3\" >0.0</td>\n      <td id=\"T_9eec5_row0_col4\" class=\"data row0 col4\" >None</td>\n      <td id=\"T_9eec5_row0_col5\" class=\"data row0 col5\" >0</td>\n      <td id=\"T_9eec5_row0_col6\" class=\"data row0 col6\" >0.848717</td>\n      <td id=\"T_9eec5_row0_col7\" class=\"data row0 col7\" >0.674810</td>\n    </tr>\n    <tr>\n      <td id=\"T_9eec5_row1_col0\" class=\"data row1 col0\" >1</td>\n      <td id=\"T_9eec5_row1_col1\" class=\"data row1 col1\" >Race</td>\n      <td id=\"T_9eec5_row1_col2\" class=\"data row1 col2\" >1.0</td>\n      <td id=\"T_9eec5_row1_col3\" class=\"data row1 col3\" >0.0</td>\n      <td id=\"T_9eec5_row1_col4\" class=\"data row1 col4\" >None</td>\n      <td id=\"T_9eec5_row1_col5\" class=\"data row1 col5\" >0</td>\n      <td id=\"T_9eec5_row1_col6\" class=\"data row1 col6\" >0.624894</td>\n      <td id=\"T_9eec5_row1_col7\" class=\"data row1 col7\" >0.674810</td>\n    </tr>\n    <tr>\n      <td id=\"T_9eec5_row2_col0\" class=\"data row2 col0\" >0</td>\n      <td id=\"T_9eec5_row2_col1\" class=\"data row2 col1\" >Gender</td>\n      <td id=\"T_9eec5_row2_col2\" class=\"data row2 col2\" >1.0</td>\n      <td id=\"T_9eec5_row2_col3\" class=\"data row2 col3\" >0.0</td>\n      <td id=\"T_9eec5_row2_col4\" class=\"data row2 col4\" >Mortgage</td>\n      <td id=\"T_9eec5_row2_col5\" class=\"data row2 col5\" >1</td>\n      <td id=\"T_9eec5_row2_col6\" class=\"data row2 col6\" >0.841285</td>\n      <td id=\"T_9eec5_row2_col7\" class=\"data row2 col7\" >0.642950</td>\n    </tr>\n    <tr>\n      <td id=\"T_9eec5_row3_col0\" class=\"data row3 col0\" >1</td>\n      <td id=\"T_9eec5_row3_col1\" class=\"data row3 col1\" >Race</td>\n      <td id=\"T_9eec5_row3_col2\" class=\"data row3 col2\" >1.0</td>\n      <td id=\"T_9eec5_row3_col3\" class=\"data row3 col3\" >0.0</td>\n      <td id=\"T_9eec5_row3_col4\" class=\"data row3 col4\" >Mortgage</td>\n      <td id=\"T_9eec5_row3_col5\" class=\"data row3 col5\" >1</td>\n      <td id=\"T_9eec5_row3_col6\" class=\"data row3 col6\" >0.996566</td>\n      <td id=\"T_9eec5_row3_col7\" class=\"data row3 col7\" >0.642950</td>\n    </tr>\n    <tr>\n      <td id=\"T_9eec5_row4_col0\" class=\"data row4 col0\" >0</td>\n      <td id=\"T_9eec5_row4_col1\" class=\"data row4 col1\" >Gender</td>\n      <td id=\"T_9eec5_row4_col2\" class=\"data row4 col2\" >1.0</td>\n      <td id=\"T_9eec5_row4_col3\" class=\"data row4 col3\" >0.0</td>\n      <td id=\"T_9eec5_row4_col4\" class=\"data row4 col4\" >Balance</td>\n      <td id=\"T_9eec5_row4_col5\" class=\"data row4 col5\" >2</td>\n      <td id=\"T_9eec5_row4_col6\" class=\"data row4 col6\" >1.010577</td>\n      <td id=\"T_9eec5_row4_col7\" class=\"data row4 col7\" >0.658610</td>\n    </tr>\n    <tr>\n      <td id=\"T_9eec5_row5_col0\" class=\"data row5 col0\" >1</td>\n      <td id=\"T_9eec5_row5_col1\" class=\"data row5 col1\" >Race</td>\n      <td id=\"T_9eec5_row5_col2\" class=\"data row5 col2\" >1.0</td>\n      <td id=\"T_9eec5_row5_col3\" class=\"data row5 col3\" >0.0</td>\n      <td id=\"T_9eec5_row5_col4\" class=\"data row5 col4\" >Balance</td>\n      <td id=\"T_9eec5_row5_col5\" class=\"data row5 col5\" >2</td>\n      <td id=\"T_9eec5_row5_col6\" class=\"data row5 col6\" >0.603506</td>\n      <td id=\"T_9eec5_row5_col7\" class=\"data row5 col7\" >0.658610</td>\n    </tr>\n    <tr>\n      <td id=\"T_9eec5_row6_col0\" class=\"data row6 col0\" >0</td>\n      <td id=\"T_9eec5_row6_col1\" class=\"data row6 col1\" >Gender</td>\n      <td id=\"T_9eec5_row6_col2\" class=\"data row6 col2\" >1.0</td>\n      <td id=\"T_9eec5_row6_col3\" class=\"data row6 col3\" >0.0</td>\n      <td id=\"T_9eec5_row6_col4\" class=\"data row6 col4\" >Amount Past Due</td>\n      <td id=\"T_9eec5_row6_col5\" class=\"data row6 col5\" >3</td>\n      <td id=\"T_9eec5_row6_col6\" class=\"data row6 col6\" >0.815159</td>\n      <td id=\"T_9eec5_row6_col7\" class=\"data row6 col7\" >0.579380</td>\n    </tr>\n    <tr>\n      <td id=\"T_9eec5_row7_col0\" class=\"data row7 col0\" >1</td>\n      <td id=\"T_9eec5_row7_col1\" class=\"data row7 col1\" >Race</td>\n      <td id=\"T_9eec5_row7_col2\" class=\"data row7 col2\" >1.0</td>\n      <td id=\"T_9eec5_row7_col3\" class=\"data row7 col3\" >0.0</td>\n      <td id=\"T_9eec5_row7_col4\" class=\"data row7 col4\" >Amount Past Due</td>\n      <td id=\"T_9eec5_row7_col5\" class=\"data row7 col5\" >3</td>\n      <td id=\"T_9eec5_row7_col6\" class=\"data row7 col6\" >0.553123</td>\n      <td id=\"T_9eec5_row7_col7\" class=\"data row7 col7\" >0.579380</td>\n    </tr>\n    <tr>\n      <td id=\"T_9eec5_row8_col0\" class=\"data row8 col0\" >0</td>\n      <td id=\"T_9eec5_row8_col1\" class=\"data row8 col1\" >Gender</td>\n      <td id=\"T_9eec5_row8_col2\" class=\"data row8 col2\" >1.0</td>\n      <td id=\"T_9eec5_row8_col3\" class=\"data row8 col3\" >0.0</td>\n      <td id=\"T_9eec5_row8_col4\" class=\"data row8 col4\" >Delinquency Status</td>\n      <td id=\"T_9eec5_row8_col5\" class=\"data row8 col5\" >4</td>\n      <td id=\"T_9eec5_row8_col6\" class=\"data row8 col6\" >0.827541</td>\n      <td id=\"T_9eec5_row8_col7\" class=\"data row8 col7\" >0.670640</td>\n    </tr>\n    <tr>\n      <td id=\"T_9eec5_row9_col0\" class=\"data row9 col0\" >1</td>\n      <td id=\"T_9eec5_row9_col1\" class=\"data row9 col1\" >Race</td>\n      <td id=\"T_9eec5_row9_col2\" class=\"data row9 col2\" >1.0</td>\n      <td id=\"T_9eec5_row9_col3\" class=\"data row9 col3\" >0.0</td>\n      <td id=\"T_9eec5_row9_col4\" class=\"data row9 col4\" >Delinquency Status</td>\n      <td id=\"T_9eec5_row9_col5\" class=\"data row9 col5\" >4</td>\n      <td id=\"T_9eec5_row9_col6\" class=\"data row9 col6\" >0.576928</td>\n      <td id=\"T_9eec5_row9_col7\" class=\"data row9 col7\" >0.670640</td>\n    </tr>\n    <tr>\n      <td 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       "layout": "IPY_MODEL_e52ac2ab23f642dca5aa9994228c90ac"
      }
     },
     "bcc2f861f20a45a7a8343dcff05b396a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "overflow": "visible",
       "width": "80px"
      }
     },
     "bccc9e629d5f470a90e34a8e6da85a7f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "overflow": "visible",
       "width": "80px"
      }
     },
     "bcddf30db2984bc9b9e67665e54a4cab": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "bcdeec75166d44ddb6994e774d3f7611": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "bce1aa95b4294d0797db70447d9f989c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "bce72c1c1d904ac8a5a146ae1b5a8973": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "width": "40%"
      }
     },
     "bceeb05759d84ee994439f3f3dcb1540": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "BoundedFloatTextModel",
      "state": {
       "layout": "IPY_MODEL_556f5e7d7aaf4279aeacdd177932af4f",
       "max": 1000,
       "step": 0.01,
       "style": "IPY_MODEL_1d63fab20535492ea1ba4c3b007642eb",
       "value": 0.8
      }
     },
     "bcf0c2840f474d63920091352e7d2650": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "bcf3a3f866f24e7491ce62415ecf65ca": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_a3ea97ee96334056babf18e23047dc3f",
       "outputs": [
        {
         "data": {
          "image/png": 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AAAgBBKyTbd8uHTwoFRRU62m9AessAhYAAEGPgHWy1q2lOnXs9Qir0cK0dnqp++/03xbnVut5AQCA8xiDVVxBgZSVZd+v5pncFzdsp8UNq29gPQAA8B/HW7AmTJggj8eju+66y+lD+19mZtF9lsoBAABnyNGAtWjRIr388svq0KGDk4etPt45sCIipOjoaj99/aP71WXHGsXkZlf7uQEAgHMcC1iZmZkaPny4Xn31VdWqVcupw1avjAz7Nj5e8niq/fTT37pHH719r1ru31rt5wYAAM5xLGDddtttGjx4sPr37+/UIaufiYWei2GqBgAAQoMjg9ynTZumpUuXatGiRRXaPycnRzk5Ob7HGd6WI9O8A9xr1jRy+l3xdSUxVQMAAMGuygFr27ZtuvPOO/XVV18pJiamQq8ZP368xo0bV9VTO69+femmm6QGDYycfgctWAAAhIQqB6wlS5Zo79696tKli++5/Px8zZ07Vy+88IJycnIUHh5e4jVjxozRqFGjfI8zMjKUlpZW1VKqrm1b6V//MnZ6uggBAAgNVQ5YF154oX7++ecSz91www1q3bq17r///lPClSRFR0cr2sC39ALdzoR6kghYAAAEuyoHrPj4eLVv377Ec3FxcapTp84pzwe8nBwpO1uKiTEyTcOuBHsMFgELAIDgxlI5xU2bJiUlScOGGTn91qQUvdT9d/p7r2skyzJSAwAAqDq/LJUzZ84cfxzW/06csG8jI42c/mh0nCb0u9HIuQEAgHNowSouN9e+jYoyWwcAAAhqBKzivC1YBgNWnazD6rJjjRoe3m2sBgAAUDUErOICIGDdM/dNffT2vfrdqtnGagAAAFVDwCouALoImQsLAIDgR8AqLgBasJgLCwCA4EfAKq59e+maa6Ru3YyV4G3BYj1CAACCl1+maQhaV15pbwaVWI/QsiSPx2g9AACg8mjBCjC74+3Z3GPzclTreIbhagAAwJkgYBWXmyvl5xst4UREpPbFJUliHBYAAMGKgFXcn/4kRURIEyYYLeOf516ph/v/SXtr1jZaBwAAODOMwSouAL5FKEmTuw01en4AAFA1tGAVFwDzYAEAgOBHC1Zxhhd79qqZc0wt9/8qWdLShm2M1gIAACqPFqziAqSLsNeW5fro7Xv14Ox/Ga0DAACcGQJWcQESsFguBwCA4EbAKi5Augi9Aate5iFF5ucarQUAAFQeY7CK69VLSkyUGjY0WsaBGonKCY9UdH6u6h89oO1JKUbrAQAAlUPAKu7xx01XYPN4tCMhWemHduqsjH0ELAAAggxdhAFqV4K9ZA7jsAAACD4ErAC1M76eJAIWAADBiC7C4lq0kLZtk779VurRw2gpn7Q9Xz+nNNPihu2M1gEAACqPgFVcdraUk2OvR2jYd0276LumXUyXAQAAzgBdhMUFyDxYAAAguBGwiguggBWRn6cuO9bokrXfS5ZluhwAAFAJ5vvCqlGT0Z+V+/NVWdmKk9Tn2e+1LWlD9RRVhoiCPH309r2SpA53TlNGTE2j9QAAgIqjBauYyPw8SVJumPncmR0Zo4OxCZL4JiEAAMGGgOVlWYoqKAxY4eYDllS0ZM5ZGXsNVwIAACojMJJEAAizCvRt0y6KKMhTdoT5MViStCMhWe33bKQFCwCAIEPAKlQQFq4RV/3VdBkl7EiwJxs96wgtWAAABBO6CAPYjkQ7YDWkBQsAgKBCwApg2wtbsBoe2WO4EgAAUBl0ERaqf3S/Zv3rVh2LjFH3kW+ZLkeStKJBCz3c/0/aVLuh6VIAAEAlELAKReXnqeaJ4/IE0KSeuxKS9WbXIabLAAAAlUQXYaGo/FxJgTNFAwAACF4ErEK+SUYDLGC13rtZl66ZyzgsAACCCAGrkDdgnQiLNFxJSaPnTNELn0zUeVt+Ml0KAACoIAJWocjCWdzzwsMNV1LSjkRmcwcAINgQsAoF0jqExXknG21IwAIAIGgEVpowKCsqVgsbtvWt/xcoticymzsAAMGGgFVoZUpzXTV8oukyTlG04DOzuQMAECzoIgxw3i7ClKP7FVaQb7gaAABQEQSsALenZm3lhoUrsiBf9TMPmi4HAABUAF2EhS5Z+73Gff2S5jXuqLuG3Gu6HJ+CsHD938DbdDg2QUdiapouBwAAVAABq1DciWNKzjqs+Jxjpks5xQcdLjJdAgAAqAS6CAtFFo5vCrSZ3AEAQPAhTRQK1HmwJCk1Y6+67FirIzE19V3TLqbLAQAAp0ELVqHIAF7suffm5Xrhk4m6adHHpksBAAAVQMAqFBXALVg7vJONMps7AABBgYBVKCKAx2DtSCi2HqFlGa4GAACcDgGr0IEaiVpZv1nALZUjSbsKa6qRm6NaxzMMVwMAAE4n8JprDHmn8yV6p/MlpssoVU5ElPbG1VK9rEMsmQMAQBCgBStIeJfMYdFnAAACHwErSHgHujdkoDsAAAGPgFXo/jlTNOflWzR82eemSynV5K5DdMvvHtTnrXqZLgUAAJwGY7AK1cs6qCaHdynuxHHTpZRqScO2pksAAAAVRAtWoYj8wJ2mAQAABBfSRCHfTO4BONGoJEXn5mjAhgVKzjokabDpcgAAQDkCM00YEFlQOJN7gLZgRRXk6YVPJtoPsp6V4uLMFgQAAMpEF2Eh31I5ARqwjkbHKSO6MFT9+qvZYgAAQLkIWIUiA3gtQi/vkjkELAAAAhsBq9Du+DraVCtVGTE1TZdSpu2Fc2ERsAAACGyB21xTzUZderfpEk7LO5s7AQsAgMBGC1YQIWABABAcCFhBZAddhAAABAUCVqEp74/VF6+PVJu9m0yXUqZFDdvplt89KL3yiulSAABAORiDVaj5gW1qmLHX923CQLSvZi191eJcqV0706UAAIBy0IJVyDvRaF4AT9MAAACCAwGrkLfl6kSATjTqdcGmJdLEidK6daZLAQAAZQjsNFGNIgJ8JnevmxbNkD5YJtWrJ7VqZbocAABQClqwCkUF+FqEXju9s7lv22a2EAAAUCYCVqFgWCpHKhawtm41WwgAAChTYKeJauKxCrQrvq4iCvJ0IjzSdDnlogULAIDAR8CSZHnC1PvW102XUSE7aMECACDg0UUYZEp0EVqW2WIAAECpCFhBZnd8XftOVpZ06JDZYgAAQKnoIpRU+9gRTf7gEWVHROnq4U+YLqdcORFR0uefSw0aSPHxpssBAAClIGBJis3NUcfd63U8Itp0KRUzaJDpCgAAQDnoIpQUmZ8rKfDnwAIAAMGBRKHic2CFG66kgpYtk77+WmrRQho2zHQ1AADgJLRgKXhmcfeZM0e67z5p6lTTlQAAgFIQsFSsBSvAJxn1adTIvmUuLAAAAhIBS1JEYQvWiWBpwfIGLGZzBwAgIBGwCh2ITdCRmJqmy6gYb8DauVPKzTVbCwAAOEWQNNn41+KG7dT1jndNl1FxyclSdLSUkyPt2CE1aWK6IgAAUAwtWMEoLExKS7PvMw4LAICAU+WA9eKLL6pDhw5KSEhQQkKCevbsqS+++MKJ2lAeBroDABCwqtxF2LBhQ02YMEEtWrSQZVl64403NHToUC1btkzt2rVzoka/6715mUbOf0/LU1tpQt8bTJdTMU89JXk8UvPmpisBAAAnqXLAGjJkSInHjz32mF588UX9+OOPQROw6mce1LnbVio7WJbKkaTOnU1XAAAAyuDoIPf8/Hx98MEHysrKUs+ePZ08tF+xVA4AAHCSI4ni559/Vs+ePZWdna2aNWtq+vTpatu2bZn75+TkKCcnx/c4IyPDiTLOWERBvqQgmgdLknbvlt56y/4m4YMPmq4GAAAU48i3CFu1aqXly5drwYIFuvXWWzVixAitXr26zP3Hjx+vxMRE35bm/UacIVGFM7nnhQVRwDp82F4u54knJMsyXQ0AACjGkYAVFRWl5s2bq2vXrho/frw6duyo559/vsz9x4wZoyNHjvi2bYZnJI8sCMIuQm8ozcyUjhwxWwsAACjBL4mioKCgRBfgyaKjoxUdHTgDyiPyg7CLMC5OqlNHOnDAnqohKcl0RQAAoFCVE8WYMWM0aNAgNWrUSEePHtW7776rOXPm6Msvv3SivmpheTw6HhGtnIgo06VUTqNGdsDatk3q0MF0NQAAoFCVA9bevXt13XXXadeuXUpMTFSHDh305ZdfasCAAU7UVy3+2fMq/bPnVabLqLxGjaRly5hsFACAAFPlgPXaa685UQfOBLO5AwAQkFiLMJgRsAAACEhBNKrbf65f/In6bFmmj9r9Rp+16WO6nIr7n/+R+veXmjQxXQkAACiGgCWp7d5NunDjIi1uWPbkqAEpNdXeAABAQKGLUFJk4USjJ4JpolEAABCwCFiSIguXygmqiUa9nntOuv12ac8e05UAAIBCQZgonFe02HOk4UrOwPPPS1u2SNdcI9Wvb7oaAAAgWrAkFXURBmULFt8kBAAg4BCwVGwMVjAHLMPrOQIAgCIELEkRlj0GKy8YB7nTggUAQMAJwkThvN//frzCCvJleTymS6k8AhYAAAGHgFWoICzcdAlnhoAFAEDAoYsw2BGwAAAIOLRgSXrkq5dU99gRPdv7f7SxTprpciqneXNp2TIpLcjqBgAghNGCJanfpsW6dO13SsjOMl1K5UVHS506SXXqmK4EAAAUImApyKdpAAAAAYeApaKAlResAeujj+zlcr780nQlAABABCxJUpR3qZxgnAdLkmbPll54QZozx3QlAABABCxJUkThYs9B20XYtKl9u3mz2ToAAIAkApakYl2EwdqCRcACACCgELAsS1EFQbzYs0TAAgAgwARponBWq7s/UkR+no5FxZgu5cx4A9a+fVJmplSzptl6AABwOVqwPB7lREQpK7qGLE+QXo7ERKlWLfv+li1GSwEAAASs0OFtxSJgAQBgnOu7CBOyM/XI1y/rRHikRg+6w3Q5Z+6DD6SkpKKWLAAAYIzrW7DiThzX71bN1m9XfWO6lKpJT5dq15Y8HtOVAADgeq4PWN4pGnLDIw1XAgAAQgUByxuwgnUOLK8tW+zlckaONF0JAACuR8Aq8K5DGG64kirKzraXy3njDcmyTFcDAICrEbAKW7BOhAV5F2GTJvZtZqZ04IDRUgAAcDsCVn6Qz+LuFRMjNWhg32dGdwAAjCJgBfsyOcWxZA4AAAEhBFJF1Sxu2Fad7nhXYaEwbqlpU+mHHwhYAAAY5vqAlR8WrsOxCabLcAazuQMAEBBc30UYUrwBa9cus3UAAOByrm/B6rDrF1294r/6pW5jvdF1iOlyqubKK6WhQ+0Z3QEAgDGub8FKP7hDw5fP1IUbFpouperi46U6dVguBwAAw1wfsCLzcyWFyLcIAQBAQCBgFeRLCqGA9dhj0mWXSUuXmq4EAADXImCFylqEXrNmSf/5j7RqlelKAABwLQJWqHURMtkoAADGuT5gRYVaCxYBCwAA41wfsCJCbQwWAQsAAONCJFWcude7DdW/z75Q2RHRpktxBgELAADjXB+wMqNrKDO6hukynOMNWNu3S7m5UmSk2XoAAHAh13cRhpyUFCkmRoqNlXbvNl0NAACu5PoWrMFrvlPHXb/o2/Sumtekk+lyqs7jkXbulJKSmNEdAABDXN+C1WfLMv1x0XR12rnOdCnOqVWLcAUAgEGuD1iRBYXTNITKtwgBAIBxBKz8EAxYs2dLQ4dKY8aYrgQAAFcKoVRxZooCVgh92+7gQemTT6Q9e0xXAgCAK9GCVbhUzolQmcldYi4sAAAMI2CF2kzuUlHA2rtXysoyWwsAAC5EwCrsIswLCzdciYNq1ZISE+37W7YYLQUAADcKoWabMzNq8CjVPHFMe2vWNl2Ks5o2lZYvt7sJ27UzXQ0AAK7i+oC1O6Gu6RL8Iz29KGABAIBq5fouwpDVvLkUFycdO2a6EgAAXMf1LVg3LZqhhOwsfdChv7Yn1jddjnPGjZMmTGBGdwAADHB9wPrDss/U9NAuzW3aObQCVkyM6QoAAHAt13cRRuaH4DQNAADAKAJWgXeahhAMWNdfL519trRmjelKAABwFQJW4TxYJ0KxBWvVKmnlSmndOtOVAADgKq4PWBGhuNizV8uW9u0vv5itAwAAl3F9wIoqIGABAABnuT5gebsIc0NxDBYBCwAAI0IwVVTOkBHPKaIgT4diE0yXUmFNRn9Wof3a796rTyXtW/qzzqnga7y2TBh8BpUBAACJgKXV9dNNl+A3m2ulSpKSsw4rPidLR6PjDFcEAIA7uD5ghbKs6BraktRAxyOjVetYBgELAIBq4uqAFXsiWzcu/lh54eF6ufvlIbmsTN8/vhKS7wsAgEDm6oAVn5Ole797S3meML3c4wrT5fgH4QoAgGrn6m8RFk3REGm4EgAAEEpcHbAiQ3mS0UJn71qvzyffrvffuc90KQAAuEboJosK8M3iHhZuuBL/ORYVo7Z7NyszKlayLLoMAQCoBq5uwQrpWdwLbU1KUb4nTDVPHFdy1iHT5QAA4AquDlhFXYShOwYrNzxS2xLrS5LSD+4wXA0AAO7g8oCVK0k6EcItWJK0ubY94WhTAhYAANUitJPFaaypl67Lh09UXgiPwZKkzbXOUj8tUdNDO02XAgCAK7g6YGVG19CShm1Nl+F3m2qfJYkuQgAAqouruwjdYkPdNP1Sp5G2J9YzXQoAAK7g6has9APb1XfTEu1ITNaXLc8zXY7f/Niogy66+Z+mywAAwDVc3YLVfs9GPfzNq7p26WemSwEAACHE1QHLO01DXphLGvIsyze5KgAA8B+XByx7moZQnmjUa/Ts1/Xzc1fpOlrrAADwO3cHrMKZ3EN9HixJyguPUPyJ42p6iG8SAgDgb64OWFEu6iLcXMueqoHJRgEA8D9XB6wIF6xF6FU0mzuTjQIA4G9VDljjx4/XOeeco/j4eNWrV0/Dhg3TunXrnKjN77yD3N3QReidbPSso/sUk5ttuBoAAEJblQPWt99+q9tuu00//vijvvrqK+Xm5uqiiy5SVlaWE/X51Yy2fXXtVX/VG12HmC7F7w7HJuhQTLwkqcmhXYarAQAgtFW56WbmzJklHk+ZMkX16tXTkiVLdP7551f18H61PSlF25NSTJdRbTbXTlWtnevU9OAOra3X1HQ5AACELMf7xo4cOSJJql27ttOHRhX92OhsHYxNUEZMTdOlAAAQ0hwNWAUFBbrrrrvUq1cvtW/fvsz9cnJylJOT43uckZHhZBkVds62lUo/uEM/p7TQ6vrpRmqoThMvuN50CQAAuIKj3yK87bbbtHLlSk2bNq3c/caPH6/ExETflpaW5mQZFfbbVXP0xMx/6MINC4ycHwAAhCbHAtbIkSP16aefavbs2WrYsGG5+44ZM0ZHjhzxbdu2bXOqjErxfoswNzzSyPmNsCzVyTosWZbpSgAACFlV7iK0LEu33367pk+frjlz5qhp09MPno6OjlZ0dHRVT11lbpoHS5I8VoEWTBqhelmH1PPWydqVkGy6JAAAQlKVW7Buu+02vf3223r33XcVHx+v3bt3a/fu3Tp+/LgT9fmVrwUrLNxwJdXD8oTpSOEA9+YHzLQaAgDgBlUOWC+++KKOHDmivn37qkGDBr7tvffec6I+v4oqcF8X4YY69ng3AhYAAP7jSBdhsIrId1cXoSRtrGOPjyNgAQDgP65ei9BNS+V4FbVgbTdcCQAAocs9yaIUz/Yerrc7X6KfGzQ3XUq18QasdAIWAAB+4+qAtbRhG9MlVLuNte0uwuRjh5V4/KiOxMYbrggAgNDj6oDlRsejYvRRu346FJugyMJB/gAAwFmuDlj91y9QjdxszWvcUQfikkyXU21GXXq36RIAAAhprh7kPmbOZP39P0+q2UHGIwEAAOe4OmBF5udKknLD3NeQl5CdqWb7maoBAAB/cHXAiijIl+SuaRokqev21Vrx/DWa8u9HTJcCAEBIcnXA8s6DleeygLW59lmSpLOO7FVMbrbhagAACD2uDlhRLu0iPBiboEMx8QqTpWYHd5guBwCAkOPqgOWdpsBtXYTyeHwTjjZjyRwAABzn7oDl7SJ0WQuWJG0oXJOwGTO6AwDgOPcli2L+cundiszP0+HYmqZLqXZFaxLSggUAgNNcHbA+bXO+6RKM2UjAAgDAb1wdsNxsdb2meqvzJVqb3MR0KQAAhBzXBqyovFz9ZuNC5YVF6Ovm3SWPx3RJ1WpvfB09dNGfTZcBAEBIcm3ASsjJ1EszxqtAHqXf94npcgAAQAhxbcDyfoMwNzzCda1XXjVOHFfzA9t0PCJa65Mbmy4HAICQ4dppGkoELJe6cfHH+uTNUfrTwo9MlwIAQEghYLlwDiyvjbXtubD4JiEAAM5yb8AqoAWrxGzulmW4GgAAQod7AxYtWNpSO1V5njDFnziuepkHTZcDAEDIIGC5uAUrNzxSv9ZqIIluQgAAnOTadLG1Voruu/gOZUdGmy7FqE21G6rZwR1qfmCbfmjSyXQ5AACEBNcGrP1xtfR+x4tMl2HchjppGrBhgVrQggUAgGNcG7Bg+6p5Dx2okaCFae1NlwIAQMhwbcCqd/SA2u7drH1xSVqV0tx0OcYsbdhGSxu2MV0GAAAhxbWD3M/dtlJT/v2I/m/O66ZLAQAAIca1ASsmN0eSlB3h7kHuktTo0C5dtvpbtdj3q+lSAAAICe4NWHl2wDpOwNKdP0zV3//zpAb98oPpUgAACAkuDlgnJEk5kVGGKzFvTXITSVLrvZvNFgIAQIhwbcCK9XURErDW1EuXJLXZR8ACAMAJrg1Y3hYsugiltYUtWI0P7VaNE8fNFgMAQAhwccAqbMFy+UzuknQgLkl742opTJZa7t9quhwAAIKea+fB+rxVL/2a1EArUlqYLiUgrE1uonpZh9Rm72YtT21luhwAAIKaawPWorT2WsTs5T5r6jXV+VuWqTXjsAAAqDLXBiyUNL1dPy1u2FYrXDyrPQAATnFtwGq3e4Nq5GZrQ500HaqRaLoc49bWa6q19ZqaLgMAgJDg2kHuD85+TR+8O1q9fv3JdCkAACDEuLYFKybXnqaBpXKK9Nj6s87Zvkpzm3aRNNh0OQAABC33Bqw8Jho92VUr/qvLV802XQYAAEHPtV2EvrUImQfLZ03hGKw2LJkDAECVuDdg+boIacHyWptsB6zW+7aYLQQAgCDn2oAV6+sipAXLa229JpKkpgd3SllZZosBACCIuTZgedciZKmcIvvjamlfXJLCZEmrVpkuBwCAoOXaQe5Pnn+dYnJzdCg23nQpAWVNclMlZy2TfvpJ6t7ddDkAAAQl1was184ZZrqEgORdMkcrVpguBQCAoOXagIXSvdX5Ev27/YX66plbTJcCAEDQcmXAis47oXZ7NupYZAzLw5xke1KKfScy0mwhAAAEMVcGrAYZ+/TR2/fqaFSszv7LB6bLAQAAIcaV3yLkG4TlG7pqtjRihDR3rulSAAAISu4OWMyBVaoLNi+V3nxT+u4706UAABCUXBmwYnNZh7A8a5Ob2HeWLTNaBwAAwcqVASuadQjL9VODlvadBQvMFgIAQJByZcAq6iKkBas0K1JaSGFh0vbt9gYAACrFlQGrqIuQFqzSHI+KkTp0sB/QigUAQKW5MmCtrddEz/Qerhnt+pouJXCde659++OPZusAACAIuXIerDX10rWmXrrpMgLbuedKr7wi7dtnuhIAAIKOKwMWKuCKK6TLL5dq1jRdCQAAQceVAav+0f1Kys7UvrhaOlgj0XQ5gSkuznQFAAAELVeOwbpx8Sf68vWR+tOCD02XAgAAQpArAxYTjVbQl19K550n3XKL6UoAAAgqruwijCmcaDSHgFU+y5Lmz5f27zddCQAAQcWVLVjeiUaZyf00une3b9evlw4cMFsLAABBxNUBi4lGT6N2balVK/s+E44CAFBh7gxYjMGquB497FsCFgAAFebOgJVHwKowZnQHAKDSXDnIfUa7flqW2lob6zQ0XUrg8wasBQukggJ7EWgAAFAuVwasdzsNMl1C8Dj7bKlZM3vx54wMKSnJdEUAAAQ8VwYsVEJEhLRhg+kqAAAIKq7s72l6cIcaHtmjiPw806UAAIAQ5MqA9fEbf9H3L92khkf2mC4leFiWtGOH6SoAAAgKrgxYzINVSYcPSw0aSI0aSZmZpqsBACDguS5ghRfkK6rA7hrMjmSahgpJSpKiouxvES5ebLoaAAACnusClneSUYl5sCqF+bAAAKgw9wWswu5BicWeK8UbsObPN1sHAABBwLUBKzsiSpbHdW//zJ13nn07b57dVQgAAMrkuoTBMjlnqEsXKTZWOnBAWrvWdDUAAAQ01wWsjOg4vXrOME3teLHpUoJLVFRRN+F335mtBQCAAOe6mdz31aytx35zs+kygtPw4XZLVteupisBACCguS5goQpuusl0BQAABAXXdRHWOHFcqRl7lZDNhJkAAMA/XBewfrNxkX548Ua98tGjpksJTpmZ0tdfSz/9ZLoSAAAClusClneahuORLJNzRsaOlQYMkF580XQlAAAELNcGLNYhPEN9+ti3fJMQAIAyORKw5s6dqyFDhig1NVUej0czZsxw4rB+EZ3LPFhV0ru3fbt6tT0nFgAAOIUjASsrK0sdO3bUpEmTnDicX8Uy0WjV1K0rtWlj3//+e7O1AAAQoByZpmHQoEEaNGiQE4fyO18XIWOwzlyfPtKaNXY34dChpqsBACDguG8Mlq+LkIB1xhiHBQBAuYxMNJqTk6OcnBzf44yMjGo797LU1nq3Y7aWp7astnOGHG/AWrpUysqS4uLM1gMAQIAxErDGjx+vcePGmTi1PmvTR5+16WPk3CGjcWPp1Velc86xF4AGAAAlGOkiHDNmjI4cOeLbtm3bZqIMVMXNN0sdO0phrutlBgDgtIy0YEVHRys62swYqMTjR2V5PMqKilV+WLiRGgAAQGhzpPkhMzNTy5cv1/LlyyVJmzdv1vLly7V161YnDu+oSR9P0Irnr9HgtQzQrpITJ+xuwhtvlHJzTVcDAEBAcSRgLV68WJ07d1bnzp0lSaNGjVLnzp318MMPO3F4R8UWfoswh3mwqiYiQho9Wpo82R7sDgAAfBzpIuzbt68sy3LiUH7HUjkOCQuzZ3X/5BN7uoYePUxXBABAwHDdCGUWe3aQd7qGb781WwcAAAHGdQErmqVynDNggH371VfSkSNmawEAIIC4LmDFstizczp0sNclzMmRPvzQdDUAAAQMI9M0mMQYrIppMvqzCu335+RzdN+aNZr31+c1/Jf6lTrHlgmDz6Q0AAACnutasD5pc74+aXO+MmJY3sUJn7S9QPmeMHksKSI/z3Q5AAAEBNe1YI0ZdIfpEkLK9sT66nHbG9ofV8t0KQAABAzXtWDBeYQrAABKclXA8lgFiss5pvCCfNOlhKS6WYdUJ+uw6TIAADDOVQGr4ZG9WvXcVVrx3NWmSwk5t8+bqh8njdCNiz82XQoAAMa5KmDFMEWD32yq3VARVoGGrv5WHqvAdDkAABjlqoAV65tklCkanPZ18+46GhWrhhl71W37atPlAABglKsClm8OrEhasJyWExmtmS17SZKGrZ5jthgAAAxzV8DKpQXLn6a36ytJGrz2e0Xm55otBgAAg9wVsHyzuNOC5Q8/Njpbe2rWVlJ2pvpuWmK6HAAAjHFnwKKL0C8KwsL1cZsLJElDV80xWwwAAAa5aib33fF19Gmr3volubHpUkLWex0u0ubaZ+mLVueZLgUAAGNcFbAWprXXwrT2pssIaRvrpmlj3TTTZQAAYJSruggBAACqg6sCVnhBPpNgVgfL0rVLP9UHb9+n5MxDpqsBAKDauSpg3TP3LW2eeJnGzH7ddCmhzePR71bO1jk7Vmvw2u9MVwMAQLVzVcCKKZzJPTfcVUPPjPi4beG3CVd/a7gSAACqn6sCVjTzYFWbz1r3Ub4nTJ13rVOjQ7tMlwMAQLVyVcBiotHqs69mLc1r3FGSdNkaWrEAAO7iqoAVy1I51erjtn0lScNWzZEsy2gtAABUJ1cFLO8YrBxasKrFly17Kic8Us0Pble7vZtMlwMAQLVx1Whvbxfh8UhasKpDZnQNfdHqPNXMOUYLFgDAVVwVsJY3aKWc8Cjtiq9ruhTXuOvSeySPx3QZAABUK1cFrCf6Xm+6BPchXAEAXMhVY7BgTsMje9R342LTZQAAUC3cFbAYB2REh12/6PuXbtLfP5mouJxjpssBAMDvXBWwlvxjuFY9c4WaHtxhuhRX+TmluTbWbqiEE8d09YqvTJcDAIDfuSpgxeVmKy43m6VyqpnlCdOr5wyTJN24eIbCC/LNFgQAgJ+5J2BZFjO5GzS9/W+0r0aSGmbs0yVrvzddDgAAfuWegJWd7bt7nJncq11ORJTe6jJYkvTHhR8xHg4AENLcE7COH/fdzWaiUSPe6nyJsiOidPaejTp328+mywEAwG/cE7CO2d9eyw0LV35YuOFi3OlQjUR9cHZ/ZUTV0FlH9pkuBwAAv3HPaO/CFizGX5n1bO/heuKC65UZXUNPmy4GAAA/cU/AiozU3CadlRMRaboSVztYI9F0CQAA+J17AlaTJrru6r+ZrgJeliV9843Urp1Uv77pagAAcJR7xmAhoEyY+Q/pwgulF14wXQoAAI4jYMGI2end7DuTJkmHDpktBgAAh7knYM2YoRXPXqVXP6SbMBB81aKH3T146JA0frzpcgAAcJR7AlZmphJOHFN04WzuMKsgLFyaONF+8Pe/S7/+arYgAAAc5J6AVTgPVg7TNASOQYOkfv2knBzpgQdMVwMAgGPcE7CYByvweDzSk0/a9995R1qyxGw9AAA4xHUB6zjL5ASWrl2l4cOlNm1KrBcJAEAwc888WL4WLAJWwHnhBalmTSnCPR9HAEBoc8+/aIVjsOgiDEBJSaYrAADAUe7pImzUSEtSW2trUorpSlCW7Gzp6aft8VgAAAQx97Rg3X67Lt+RbroKlGfKFOmee+ylc37zG6lBA9MVAQBwRtzTgoXAd8MNUtu20p490m9/y6B3AEDQImAhcERHSx9/LNWqJS1YIP3pT/ai0AAABBn3BKwrr9SPk67TwF9+MF0JytO8ufT++1J4uPTmm9Izz5iuCACASnNPwNqzRymZBxVWUGC6EpxO//7Ss8/a9++7T/riC7P1AABQSe4JWN55sJhoNDiMHCndfLPdbVg4xQYAAMHCPQGLebCCi8cjTZokLVwoXX656WoAAKgU9wSswhYsFnsOIlFRUvv2RY+3bfP9OQIAEMjcMw8WaxEGnCajP6vwvm33bNKUD8ZqWWor3TpsjArCwiv0ui0TBp9peQAAnDHXtWCxFmFwijtxTInZmRq4/keNnfUK0zcAAAKaewLW2WdrTXITZUbFmq4EZ2BRWnvddendKpBHI5Z+pv9d8KHpkgAAKJN7AtZ332nQjS9oX83apivBGfqidW/97cKbJUmjv52ioatmG64IAIDSuSdgISRM7jZUr5zzW0nSU58/p34bFxmuCACAUxGwEHTG97tBM9peoMiCfP1pwYeMxwIABBx3fItw+3apVy99nh2uS274h+lqUEWWJ0z3XPIXba51lv51zjB7ziwAAAKIO1qwMjOlrVt11pG9piuBQ/LCI/R87/9RVnQN33N1sw4ZrAgAgCLuCFjMgRXy/jz/fc169X/VbvcG06UAAOCugMUcWKEpMj9XfTctVmJOlt5+7yF12rnOdEkAAJdzWcBimZxQlBseqRuveETLGrRSreyjmjr1/9R//QLTZQEAXMwdAcu70HMkAStUZUbX0PBrHtXs9K6KzcvRy9Mf0x+WfW66LACAS7kjYPkWeqaLMJQdi4rVzZc/rKkdLlK4VaBH//tP6YEHTJcFAHAhdwSsGjWkNm20LbG+6UrgZ/lh4Rpz8e16uvdw+4lGjcwWBABwJY9lmZ+lMSMjQ4mJiTpy5IgSEhL8dp4moz/z27EReNrt3qDPptxZ9IRlMWcWAKBCqppN3NGCBVdaldK86MGBA1KvXtK8eeYKAgC4BgEL7vDww9L8+VK/ftLLL5uuBgAQ4ghYcIcnnpCuuELKzZX+93+lP/7RbtUCAMAPCFhwh5o1pfffl8aPt8dhvfqqlJYm/elP0tq1pqsDAIQYAhbcw+ORRo+WZs6UOnWyp+945RXp82LzZZ04YQ+GBwCgCiJMFwD4U5nfHL3ob+redpX+sOxzPbj1LGUU7nfvt2/o2mWfa2PthtpYp6HW103TN+nnaH1y4zLPsWXCYH+UDgAIYgQsuJPHo4Vp7bUwrX2Jp9MP7lBCTpY671qnzrvsNQ3HzJmidXUb6bPWffR6t6HKjK5homIAQBAhYAHF3DXkHj17aKeaHdiu9IM71HnnWp2/eZla7d+qBotm6KUeV/j2jcjPU144f4UAAKfiXwegmJyIKP2S3ES/JDfxPZeQnakB6xcoMTtTJyIi7SctS59NuUNbkxpI5+ZKgwdLkZFmigYABBwCFnAaGTE19eHZF5Z4ru3ezWq1f6ta7d8q/fa3UnKyPcdWjx5S9+5Sly72Ek0AAFfiW4TAGVhdP139b/qnXur+OyklRdq3z54G4u67pT59pAkTTJcIADCIgAWcoQ11G2lCvxulbdukb76RHn9cGjZMatDAbsnymjPHfvzCC3YQAwCEPLoIgaqKiLC7B/v1sx9bVsm5tN5+W1q40N7+8hepdWspPd3emjWTrr1WSkw0UzsAwC8cC1iTJk3Sk08+qd27d6tjx476xz/+oe7duzt1eCBglTnXVqG6sX015MIw/XbVbHXYvUFaudLeCnVaX0eHY+2V2u/8/l31+nW5tiWl6GBsgo5FxiorKkb/d2U3ezb63/5Wio/36/sBAFSdIwHrvffe06hRo/TSSy+pR48eeu655zRw4ECtW7dO9erVc+IUQNDaH1dLk7sN1eRuQ9XwyB6lH9iuRkf2KO3wbqUcPaDDMUWBqf2eDeq+fbW6b19d8iBzJtu3l11W9NyDD0qzZkmNG9tbo0ZFW0qKVK+ePXs9AKDaeSyr6uuC9OjRQ+ecc45eeOEFSVJBQYHS0tJ0++23a/To0ad9fUZGhhITE3XkyBElJCRUtZwyna6lATCt1b4tarF/qxod3q2E7EzVyM1RXO5xXd4ySSookKZPL9p58OCSy/ycLDtbio627//lL9KXX0qxsfa3G723cXF29+STT9r3JenHH6UdO6SYGHuLjZWioqSwMDuwnX223S0qSbt2SRkZUni4vUVEFL0mNtZ+DgCCUFWzSZVbsE6cOKElS5ZozJgxvufCwsLUv39/zZ8/v6qHB1xlXXITrSs2B5fX3d47xf6T0CJlsJoN66izMvYpNWOfzsrYa98e2au43Gy1Hvu1b99XP52vARvWlHneljUu0i9PDbMfvPCC9M47ZRd58KBUq5Z9f+xYe+HssmzZYreuSfaXAN54oyi4RUaW3F5+WWrY0N73ueekKVPskFhQUDSmzXs7Y4bUvnAW/unT7W9wejxSXp6Un29veXl24JswwR73JkmzZ9v7R0SU3MLD7ddfd53UpIm979KldiiV7J95PHbI9O5/6aX2ODpJ2rhR+uEH++fh4fatdyyeZUm9etkti5K0das0b17J4xbfunUrqmHXLntf73G818K7desmtWpl77t3r/3+iv+8+P5du0rt2tn77t8vffrpqX9eHo9df4cO9ibZAfqrr07dz6tFCzt0S9KxY/Zan2Vp2lTq3Nm+n5MjfVbOf3obNbLfn2T/WX7yScn3VnxLS7Ovsde775a8pt7/HHg8dutu8X0//tj+vJSmbl3p/POLHn/2mV13aZKSpN/8pujxzJn29ShNfLw0YEDR46++ko4eLX3f2Fhp0KCix7NnS4cOlb5vVJT9ufSaO9f+sy5NeLg0dGjR4x9+kHbvLn1fyR6e4P1zX7DA/k9YWS67rOg/YUuWSL/+Wva+l1xi/z6QpOXL7b9LXsX/3luW/Z9K738EV64s+h0QoKocsPbv36/8/HzVr1+/xPP169fX2rVrS31NTk6Ocop9SI8cOSLJTov+VJBTxocdCELrEpK1LiG59B9allTs8/7oeVfr5Y4DFZV3QjF5JxSTm6OY/FzFnjiumieylZ2fq0Z/+UCS9KeNueqZ2lrReScUnZer6PwTisrPlcey5JF02QOfKKtwuaDRi7bpsqga8lgFCrcKFF6Qr+iCon+szn/8vzpYI0mS9H+zv9X//PJLme/n4oema3tSiiTpju+/1R9/+qnMfS+b+F9tqmMHxj/Pf19/XvDvMve9Jqa7VjZoIUm6fvHHuuf7ssPjjSvyfcsnXf3Tl3po9mtl7quUFPsfYMn+B/LWW8ved/Jk6Xe/s+/Pni1df33Z+06aJP3hD/b9efOkK68se98nn7S/tSpJixZJ11xT9r7jxtlBRJJ+/lm64Yay973//qKQt3atdMUVZe97++3So4/a97dulS6/vOx9b7pJeuYZ+/7+/eXve801duiW7KBS3r6XXSa99VbR4+HDy963f3/pww+LHv/+9/bC76U57zzpiy+KHo8YIR04UPq+nTvb3xj2+tOf7OtRmpYt7T8vr9tvl9atK33fhg2lVauKHt9zjx3+S1O7trR5c9HjBx6Qvv++9H1jYqQ9e4oeP/LIqUG6uMOHiwLWhAn2f3LKsnNnURB65hk78JZlwwZ7HkHJ/uz/619l77tiRdF/2N5/v+g/LX7izSRn3NFnVdGOHTssSdYPP/xQ4vl7773X6t69e6mvGTt2rCWJjY2NjY2NjS2gt23btp1RPqpyC1bdunUVHh6uPcWTsKQ9e/YoJSWl1NeMGTNGo0aN8j0uKCjQwYMHVadOHXlcPig3IyNDaWlp2rZtm1/HowUzrtHpcY1Oj2t0elyj0+ManV6wXiPLsnT06FGlpqae0eurHLCioqLUtWtXzZo1S8OGDZNkB6ZZs2Zp5MiRpb4mOjpa0d7Bt4WSkpKqWkpISUhICKoPoglco9PjGp0e1+j0uEanxzU6vWC8RolVmKPQkWkaRo0apREjRqhbt27q3r27nnvuOWVlZemG8vr4AQAAQpQjAevqq6/Wvn379PDDD2v37t3q1KmTZs6cecrAdwAAADdwbCb3kSNHltkliIqLjo7W2LFjT+lCRRGu0elxjU6Pa3R6XKPT4xqdnluvkSMTjQIAAKBImOkCAAAAQg0BCwAAwGEELAAAAIcRsKrBpEmT1KRJE8XExKhHjx5auHBhmfu++uqr6tOnj2rVqqVatWqpf//+p+x//fXXy+PxlNguvvhif78Nv6rMNZoyZcop7z/Gu5ZVIcuy9PDDD6tBgwaKjY1V//79tX79en+/Db+qzDXq27fvKdfI4/Fo8ODBvn1C6XM0d+5cDRkyRKmpqfJ4PJpR3jIehebMmaMuXbooOjpazZs315QpU07ZpzLXPNBV9hp99NFHGjBggJKTk5WQkKCePXvqS+/ajIUeeeSRUz5Drb3rPgahyl6jOXPmlPr3bPdJa/q5+XNU2u8Zj8ejdt51MRV6nyMvApafvffeexo1apTGjh2rpUuXqmPHjho4cKD27t1b6v5z5szR73//e82ePVvz589XWlqaLrroIu04aWHNiy++WLt27fJtU6dOrY634xeVvUaSPWFd8ff/60mLiU6cOFF///vf9dJLL2nBggWKi4vTwIEDlZ2d7e+34xeVvUYfffRRieuzcuVKhYeH68qT1rULlc9RVlaWOnbsqEmTJlVo/82bN2vw4MHq16+fli9frrvuuks333xziQBxJp/LQFbZazR37lwNGDBAn3/+uZYsWaJ+/fppyJAhWrZsWYn92rVrV+Iz9H1Za98FgcpeI69169aVuAb16tXz/cztn6Pnn3++xLXZtm2bateufcrvolD6HPmc0QI7qLDu3btbt912m+9xfn6+lZqaao0fP75Cr8/Ly7Pi4+OtN954w/fciBEjrKFDhzpdqjGVvUaTJ0+2EhMTyzxeQUGBlZKSYj355JO+5w4fPmxFR0dbU6dOdazu6lTVz9Gzzz5rxcfHW5mZmb7nQu1z5CXJmj59ern73HfffVa7du1KPHf11VdbAwcO9D2u6jUPZBW5RqVp27atNW7cON/jsWPHWh07dnSusABSkWs0e/ZsS5J16NChMvfhc1TS9OnTLY/HY23ZssX3XKh+jmjB8qMTJ05oyZIl6t+/v++5sLAw9e/fX/Pnz6/QMY4dO6bc3FzVrl27xPNz5sxRvXr11KpVK9166606UNYK7wHuTK9RZmamGjdurLS0NA0dOlSriq02v3nzZu3evbvEMRMTE9WjR48KX/dA4sTn6LXXXtM111yjOO8K94VC5XNUWfPnzy9xPSVp4MCBvuvpxDUPNQUFBTp69Ogpv4vWr1+v1NRUpaena/jw4dq6dauhCs3p1KmTGjRooAEDBmjevHm+5/kcneq1115T//791bhx4xLPh+LniIDlR/v371d+fv4pM9rXr1//lD76stx///1KTU0t8Rf04osv1ptvvqlZs2bpiSee0LfffqtBgwYpPz/f0fqrw5lco1atWun111/Xxx9/rLffflsFBQU677zztH37dknyva4q1z2QVPVztHDhQq1cuVI333xziedD6XNUWbt37y71emZkZOj48eOO/N0NNU899ZQyMzN11VVX+Z7r0aOHpkyZopkzZ+rFF1/U5s2b1adPHx09etRgpdWnQYMGeumll/Thhx/qww8/VFpamvr27aulS5dKcubfgFCyc+dOffHFF6f8LgrVz5FjM7nDeRMmTNC0adM0Z86cEoO4r7nmGt/9s88+Wx06dFCzZs00Z84cXXjhhSZKrVY9e/ZUz549fY/PO+88tWnTRi+//LL+9re/GawsML322ms6++yz1b179xLPu/1zhIp79913NW7cOH388cclxhcNGjTId79Dhw7q0aOHGjdurPfff1833XSTiVKrVatWrdSqVSvf4/POO08bN27Us88+q7feestgZYHpjTfeUFJSkoYNG1bi+VD9HNGC5Ud169ZVeHi49uzZU+L5PXv2KCUlpdzXPvXUU5owYYL++9//qkOHDuXum56errp162rDhg1Vrrm6VeUaeUVGRqpz586+9+99XVWOGUiqco2ysrI0bdq0Cv2SCubPUWWlpKSUej0TEhIUGxvryOcyVEybNk0333yz3n///VO6VU+WlJSkli1buuIzVJbu3bv73j+foyKWZen111/Xtddeq6ioqHL3DZXPEQHLj6KiotS1a1fNmjXL91xBQYFmzZpVogXmZBMnTtTf/vY3zZw5U926dTvtebZv364DBw6oQYMGjtRdnc70GhWXn5+vn3/+2ff+mzZtqpSUlBLHzMjI0IIFCyp8zEBSlWv0wQcfKCcnR3/4wx9Oe55g/hxVVs+ePUtcT0n66quvfNfTic9lKJg6dapuuOEGTZ06tcQUH2XJzMzUxo0bXfEZKsvy5ct975/PUZFvv/1WGzZsqNB/9kLmc2R6lH2omzZtmhUdHW1NmTLFWr16tfXHP/7RSkpKsnbv3m1ZlmVde+211ujRo337T5gwwYqKirL+/e9/W7t27fJtR48etSzLso4ePWrdc8891vz5863NmzdbX3/9tdWlSxerRYsWVnZ2tpH3WFWVvUbjxo2zvvzyS2vjxo3WkiVLrGuuucaKiYmxVq1a5dtnwoQJVlJSkvXxxx9bK1assIYOHWo1bdrUOn78eLW/PydU9hp59e7d27r66qtPeT7UPkdHjx61li1bZi1btsySZD3zzDPWsmXLrF9//dWyLMsaPXq0de211/r237Rpk1WjRg3r3nvvtdasWWNNmjTJCg8Pt2bOnOnb53TXPNhU9hq98847VkREhDVp0qQSv4sOHz7s2+fuu++25syZY23evNmaN2+e1b9/f6tu3brW3r17q/39OaGy1+jZZ5+1ZsyYYa1fv976+eefrTvvvNMKCwuzvv76a98+bv8cef3hD3+wevToUeoxQ+1z5EXAqgb/+Mc/rEaNGll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\n",
          "text/plain": "<Figure size 432x324 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        },
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      "state": {
       "height": "auto"
      }
     },
     "de119ebb283542918c43da580f145097": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_0fc5cd7a20fe4eb4bae42ee87755d314",
       "style": "IPY_MODEL_eb18f40174ab4ba48baf92e77f40a9e5",
       "value": "Protected:"
      }
     },
     "de1de32d73884acabbca27d6cb4efe90": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "overflow": "visible",
       "width": "80px"
      }
     },
     "de1efb7abd424b6cbd12aa467bf308bf": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "de1f13430bda4e1ea28746f03414009a": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_158fa9cdeef34edc90a46610ecc1f527",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x324 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        },
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\n",
          "text/plain": "<Figure size 432x432 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "e7f633a3e8eb4e26ae3767f18863a664": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "e806d02dbd6f4f32894083f2cc7c0bc6": {
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     },
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      "model_module": "@jupyter-widgets/base",
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      "model_name": "DropdownModel",
      "state": {
       "_options_labels": [
        "Select Model",
        "GLM",
        "XGB2",
        "XGB7"
       ],
       "index": 2,
       "layout": "IPY_MODEL_28af20d9dfad40299504f81a0b9785fd",
       "style": "IPY_MODEL_2fce3a29de6a42cea3741319b38e47fa"
      }
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     "ea877f6a693f4b7eb978b9706c62bd2f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": "initial"
      }
     },
     "ea8c040faa8f487eb902d631790f774e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
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      }
     },
     "ea94a1707c614e16a026599a38cc746d": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "ea9923fec2d149b3b2b93c994a0e69d6": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "ea999fd63c404277aa4cab4928ebcf96": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "ButtonStyleModel",
      "state": {}
     },
     "ea9f52d75f34419f97c0380c525c61a3": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_6155091efec643b4876d95b98b5e04e0",
       "outputs": [
        {
         "data": {
          "text/html": "<style type=\"text/css\">\n</style>\n<table id=\"T_10a65\">\n  <thead>\n    <tr>\n      <th id=\"T_10a65_level0_col0\" class=\"col_heading level0 col0\" >Group Index</th>\n      <th id=\"T_10a65_level0_col1\" class=\"col_heading level0 col1\" >Group Category</th>\n      <th id=\"T_10a65_level0_col2\" class=\"col_heading level0 col2\" >Reference Group</th>\n      <th id=\"T_10a65_level0_col3\" class=\"col_heading level0 col3\" >Protected Group</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_10a65_row0_col0\" class=\"data row0 col0\" >0</td>\n      <td id=\"T_10a65_row0_col1\" class=\"data row0 col1\" >Gender</td>\n      <td id=\"T_10a65_row0_col2\" class=\"data row0 col2\" >1.0</td>\n      <td id=\"T_10a65_row0_col3\" class=\"data row0 col3\" >0.0</td>\n    </tr>\n    <tr>\n      <td id=\"T_10a65_row1_col0\" class=\"data row1 col0\" >1</td>\n      <td id=\"T_10a65_row1_col1\" class=\"data row1 col1\" >Race</td>\n      <td id=\"T_10a65_row1_col2\" class=\"data row1 col2\" >1.0</td>\n      <td id=\"T_10a65_row1_col3\" class=\"data row1 col3\" >0.0</td>\n    </tr>\n  </tbody>\n</table>\n",
          "text/plain": "<pandas.io.formats.style.Styler at 0x2c76609c310>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "eaa2d149379441b18ecd903d7b6e621b": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_c7aa5d6123d24ff493d50d0a64d58bbc",
       "outputs": [
        {
         "data": {
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\n",
          "text/plain": "<Figure size 864x324 with 4 Axes>"
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      "model_module": "@jupyter-widgets/controls",
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      "model_name": "HTMLModel",
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       "style": "IPY_MODEL_40352a9aaf494ca7b829674cd5966d31",
       "value": "Favorable Response:"
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       "index": 0,
       "layout": "IPY_MODEL_2f0cbf1249484b3382e3d4233337ec2b",
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